
20202021 Undergraduate Academic Catalog [ARCHIVED CATALOG]
Course Descriptions




Industrial Engineering 


IE 3771  Automation Technologies 2 lecture hours 2 lab hours 3 credits Course Description This course deals with automation technologies utilized in manufacturing, logistics, and service environments. It compares manual and automated systems for material handling, storage systems, inspection, and product identification. It includes handson lab instruction in topics such as robotic programming, as well as flexible manufacturing systems, and culminates with a project that uses microcontrollers, PLCs and a CoBot to sort work product and simulate an assembly line. (prereq: IE 426 or ME 323 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand and perform basic calculations regarding analogtodigital conversion and digitaltoanalog conversion
 Distinguish important capabilities and limitations related to automation technologies, particularly with respect to robotics, identification, inspection, material handling, and storage systems
 Select and justify a material transport system and a storage system for a given scenario in a manufacturing or service industry
 Perform calculations related to production rate, production capacity, and storage capacity
 Distinguish important capabilities and limitations of robotic processes
 Program a robot using a software interface as well as online programming methods
 Understand the inverse and forward kinematics of robots and related coordinate systems
 Understand flexible automated production systems through case studies
 Understand how image processing and visual systems are used in manufacturing processes
Prerequisites by Topic
 General understanding of a variety of manufacturing processes (such as machining, sheet metal stamping and forming, and plastic injection molding)
Course Topics
 Analog and digital Introduction: ADC, DAC, microcontroller employment, basics of arrays, vectors, matrices, & systems of linear equations
 Introduction to logic control & PLCs: Boolean algebra, logic gates, truth tables, ladder logic for use in automation
 Introduction to robotics: applications, coordinate system frames, manipulator classification, forward & inverse kinematics
 Robotics & industrial control systems, robot programming, safety
 Product lifecycle management (PLM), digital manufacturing, flexible manufacturing systems, transfer lines
 flexible manufacturing systems & transfer lines case studies
 Image processing and visual systems
 Course project: employment of microcontrollers, PLC, and a CoBot to sort work product (factory mockup)
Laboratory Topics
 Analog/digital introduction via microcontrollers
 PLCs & layout of factory (Sim)
 Forward/inverse kinematics (SolidWorks Robot Sim)
 RobotStudio Sim of ABB robot
 Online programming of ABB robot & UR3 CoBots
 Visual systems investigation
Coordinator Dr. Leah Newman



IE 3820  Stochastic Processes 4 lecture hours 0 lab hours 4 credits Course Description This course continues the modeling approach to problem solving by presenting techniques used to analyze and design systems affected by random variables. Queueing theory, Markov processes, and decision theory are examined. Case studies and computer algorithms are utilized. (prereq: MA 262 and (MA 2314 or MA 231 )) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Identify and apply quantitative analysis techniques to engineering problems related to random processes
 Use quantitative management technique results to analyze alternative solutions and assist in decision making
 Have an understanding of how these methods impact business and industry
 Demonstrate systematic problemsolving skills and be able to communicate the process effectively
Prerequisites by Topic
 Understanding of basic probabilistic principles and calculations
 Familiarity with common discrete probability distributions
 An ability to take complex derivatives and limits
Course Topics
 Introduction to quantitative management
 Probability for stochastic processes
 Fundamentals of decision theory
 Decision theory and utility theory
 Queueing theory
 Markov analysis
 Dynamic programming
 Review
 Examinations
Coordinator Dr. Aaron Armstrong



IE 4001  Industrial Engineering Cooperative Practicum 1 1 lecture hours 0 lab hours 1 credits Course Description Students complete the first quarter of approved, supervised cooperative employment. A written report of the work performed is required, as well as a draft of a technical paper related to the work experience. (prereq: sophomore standing and consent of program director) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Gain professional work experience
 Present a written summary of the work experience and a related technical topic
Prerequisites by Topic Course Topics
 Depends on the specific coop assignment
Coordinator Dr. Leah Newman



IE 4002  Industrial Engineering Cooperative Practicum 2 1 lecture hours 0 lab hours 1 credits Course Description Students complete the second quarter of approved, supervised cooperative employment. A written report of the work performed is required, as well as a draft of a technical paper related to the work experience. (prereq: IE 4001 and consent of program director) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Gain professional work experience
 Present a written summary of the work experience and a related technical topic
Prerequisites by Topic Course Topics
 Depends on the specific coop assignment
Coordinator Dr. Leah Newman



IE 4003  Industrial Engineering Cooperative Practicum 3 1 lecture hours 0 lab hours 1 credits Course Description Students complete the third quarter of approved, supervised cooperative employment. A written report of the work performed is required, as well as a final technical paper related to the work experience. (prereq: IE 4002 and consent of program director) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Gain professional work experience
 Present a written summary of the work experience and a related technical topic
Prerequisites by Topic Course Topics
 Depends on the specific coop assignment
Coordinator Dr. Leah Newman



IE 4260  Design for Manufacture and Assembly 2 lecture hours 2 lab hours 3 credits Course Description Product design has become increasingly challenging with shorter design/development cycles and the need to address numerous competing concerns, including usability, maintainability, reliability, disposability, and more. This course covers design guidelines and analytical techniques that can be utilized to improve product designs with the primary goal of simplifying manufacturing and assembly processes, thus making the production operations more costeffective across the product’s lifecycle. (prereq: IE 426 or ME 323 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand the benefits associated with designing components and products with the entire product life cycle in mind
 Understand how early design decisions can influence manufacturing processes, product costs, inspection practices, and supply chains
 Evaluate and compare alternative component and assembly designs for manufacturability and cost effectiveness
 Know some of the specific design changes and design guidelines that enable a component or product to have greater manufacturability, usability, maintainability, reliability, and disposability
 Make and justify tradeoffs between competing design objectives
Prerequisites by Topic
 Knowledge of a variety of manufacturing processes
Course Topics
 Product life cycle and design objectives
 DFA
 DFM for various manufacturing processes
 Design for other objectives
 Project work
Laboratory Topics
 The 2hour weekly lab will be used to evaluate current product and component designs and to create improved designs. Students will disassemble one or more products and practice using various analytical techniques, as well as documenting new designs using CAD software
Coordinator Dr. Doug Grabenstetter



IE 4332  Lean 3 lecture hours 0 lab hours 3 credits Course Description Lean techniques can be used to improve any business process and make companies globally competitive. During this course students will learn to identify what is valueadded and what is waste in any business process and to eliminate identified waste. Students will also learn the value of teamwork in a Lean Enterprise and will be introduced to the concepts of 5S, Value Stream Mapping and Kaizen. (prereq: junior standing) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Explain lean thinking and management methods
 Follow a phased process for Lean Enterprise implementation
 Describe the seven forms of waste in business
 Explain the five principles of lean and how to implement them in business
 Understand and be able to apply the concept of value add and nonvalue add activities
 Explain and prepare a value stream map
 Explain and calculate takt time
 Explain the difference between “push” and “pull” and apply tools to accomplish pull
 Explain and apply 5S, cellular layouts, and leveling
 Explain kaizen
 Explain and apply A3
Prerequisites by Topic Course Topics
 Toyota philosophy and culture, lean leadership, and lean wastes
 People development and team building
 Process stability, flow and value stream mapping
 Standard work
 5S, cellular layouts, and level loading
 Total productivity maintenance
 Manufacturing cells and setup reduction
 Push vs. pull and kanban systems
 Kaizen and change management
 Toyota problem solving technique
Coordinator Dr. Doug Grabenstetter



IE 4336  Quick Response Manufacturing 3 lecture hours 0 lab hours 3 credits Course Description Producing products profitably in an increasingly competitive world market requires speed and agility. Companies and organizations that can get their products and services to customers quickly tend to do so more efficiently and reliably and with better quality than do slower companies. This course will develop students’ abilities to sustainably and efficiently reduce the amount of time processes take to complete. Special focus will be placed on process mapping, production modeling, product development, cellular manufacturing, and mass customization. (prereq: junior standing) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Analyze important issues and decisions related to quick response manufacturing
 Understand manufacturing system dynamics (particularly how lot size and utilization influence lead time)
 Measure Manufacturing Criticalpath Time, the QRM metric, in a variety of manufacturing, service, and logistical applications
 Discuss quick response manufacturing in the context of production and office operations
 Demonstrate knowledge of quick response manufacturing by redesigning a system or process to reduce the process lead time
 Demonstrate knowledge of MPX rapid modeling software by utilizing it for process/system analysis and QRM focused improvements
Prerequisites by Topic Course Topics
 Benefits of QRM
 Performance and time measurement
 System dynamics and response time spiral
 Reorganizing functional production departments into manufacturing cells
 Designing, implementing, and operating manufacturing cells
 Making capacity and lot sizing determinations/decisions
 Building models and analyzing results using MPX software
 Production planning in a QRM environment
 POLCA and ConWIP production control systems
 Customer and supplier relations with QRM
 Office and service cells
 New product introduction and product lifecycle with QRM principles
Coordinator Dr. Aaron Armstrong



IE 4501  Healthcare Systems Engineering 3 lecture hours 0 lab hours 3 credits Course Description Healthcare as an industry is becoming an increasingly large part of the national and world economies at the same time that healthcare costs are escalating at an unsustainable rate. The purpose of this class is to increase the student’s understanding of how to apply proven industrial engineering methods to healthcarerelated problems. Potential topics include statistical process control for medical applications; process improvement in healthcare delivery; simulation of healthcare services; timebased patient flow enhancement; resource scheduling optimization; hospital and clinic layout and facilities design; healthcare financing and cost management; and quality and other metrics for healthcare. (prereq: junior standing) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand how industrial engineering principles and methods can be applied to healthcare services
 Explain and describe major healthcare processes from an engineeringbased perspective
 Understand key performance metrics that are utilized to analyze the effectiveness of healthcare quality and delivery
 Apply engineering concepts and methods, including human factors, quality tools, operations research/simulation modeling, and facilities design, to healthcarerelated problems
 Conduct costbased comparisons and investment justifications in a healthcare environment
Prerequisites by Topic Course Topics
 Introduction to healthcare processes
 Applying engineering methods to healthcare services
 Information technology management in healthcare
 Use of bar coding, RFID, and other tracking systems in healthcare
 Human factors and medical errors
 Quality assurance and statistical process control in healthcare
 Mistakeproofing in healthcare
 Modeling of healthcare processes and systems
 Healthcare layouts and facilities design
Coordinator Dr. Leah Newman



IE 4621  SocioTechnical Systems 3 lecture hours 0 lab hours 3 credits Course Description Sociotechnical Systems (STS) is a method that might be used to analyze manufacturing and service jobs, as well as entire organizations through the study of classical theories and techniques of management and organizational behavior (i.e., Frederick Taylor’s Scientific Management, Elton Mayo’s Human Relations, etc.), as well as more recent developments related to quality of working life, change management, and the macroergonomic analysis and design process. This course includes analysis of both social and technical systems within an organization in an effort to improve the design and functionality of the entire system. (prereq: IE 3621 or SS 464 , junior standing) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Describe what engineering sociotechnical systems means, what it covers, and what shaped it as a profession
 Understand sociotechnical systems engineering theory
 Understand how to apply the sociotechnical systems theory and analytical methods to design or assist in the redesign of an organization
 Understand how to conduct a sociotechnical systems analysis of a work process
 Understand how different leadership skills impact team/group performance
 Understand how organizational culture impacts employee morale and performance
 Understand the impact of motivation and satisfaction on team/group performance
Prerequisites by Topic Course Topics
 Open and other systems
 History of sociotechnical systems
 Sociotechnical systems  The environment
 Sociotechnical systems  The social system
 Sociotechnical systems  The technical system
 Sociotechnical system design, redesign and analysis
 Macroergonomics and organizational design and participation
 Sociotechnical applications and case studies
Coordinator Dr. Leah Newman



IE 4622  Organization and Job Design 3 lecture hours 0 lab hours 3 credits Course Description Organizations are becoming increasingly more complex with regards to how business is accomplished when considering issues of cultural and emotional intelligence of employees, the impact of globalization as well as quality of working life issues. This course assists in the design, implementation, and diffusion of productive organizations and an individual’s role within the organization. (prereq: IE 3621 or SS 464 , junior standing) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand the theories associated with organization and job design
 Understand how to apply the job design theories and analytical methods in an effort to redesign a job and/or an organization
 Conduct a detailed job analysis
 Understand how different leadership skills and other organizational management approaches impact team/group performance
 Understand how organizational culture impacts employee morale and performance
 Understand the impact of motivation and satisfaction on team/group performance
Prerequisites by Topic Course Topics
 Organizational management theories  overview
 Job design theories
 Job analysis data collection methods
 Employee motivation
 Teamwork and participation
 Job redesign and case studies
 Employer/employee ethics
Coordinator Dr. Leah Newman



IE 4640  Product Design and Development Processes 3 lecture hours 0 lab hours 3 credits Course Description This course introduces students to the product design and development process from the lens of human factors engineering. It includes a team project in which students are expected to propose an original design idea, conduct a needs analysis, develop and test a prototype, and create a financial analysis of the product of interest. (prereq: junior standing) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand tools and methods used for product design and development from a human factors perspective
 Possess more confidence in developing a new product
 Be aware of the role of multiple functions in creating a new product (e.g. strategy, project management, finance, marketing, industrial design, human factors, engineering, production, supply chain, safety)
 Coordinate necessary roles, functions, resources, tasks, etc. to achieve a common goal
 Understand how to complete a needs analysis
 Understand how to develop, complete, and test a design prototype
Prerequisites by Topic Course Topics
 Human factors overview
 Design thinking
 Customer needs analysis
 Product specifications + scrum process
 Creativity and concept generation
 Product architecture
 Prototyping and testing design
 Concept selection
 Industrial design
 Experience and service design
 Design for environmental sustainability
 Product costing
Coordinator Dr. Leah Newman



IE 4773  Computer Aided Manufacturing/CNC Machining/Rapid Prototyping 2 lecture hours 2 lab hours 3 credits Course Description This course teaches students the fundamentals of computeraided manufacturing (CAM), computer numerical control (CNC) machining, and rapid prototyping (RP). Students will learn how to program a CNC machine using manual G/M code programming and computeraided manufacturing software. The course also provides an overview of rapid prototyping (freeform fabrication/additive manufacturing) technologies, and students will compare part production via RP and CNC. (prereq: IE 426 or ME 323 or consent of instructor, ME 1601 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Distinguish important capabilities and limitations of CNC machining and RP processes
 Manually write a CNC program for a CNC mill and a CNC lathe
 Use CAD/CAM software to create and execute CNC programs to machine workpieces on a CNC mill (for studentgenerated designs: 2.5D milling, holemaking, and 3D contour milling)
 Explain workholding concepts and their importance to CNC machining operations
 Select cutting tools and cutting conditions for various types of machining operations (drilling, facing, pocketing, etc.)
 Set up a CNC machining center, with oversight from a lab technician
Prerequisites by Topic
 Knowledge of machining processes (milling, drilling, turning, etc.).
 Must know how to create a part design using 3dimensional CAD software
Course Topics
 Review of machining processes
 CNC machining and programming for mills
 CAM software and project work
 Workholding
 Rapid prototyping
 CNC machining and programming for lathes
 Canned programs and quick code
 Multiaxis machining
Laboratory Topics
 The 2hour weekly lab, plus some additional lecture class periods are used for working with the CAM software package to create CNC programs. The programs are thoroughly simulated and tested before running them on a Haas VF1 machining center. Students also learn how to set up and operate the Haas.
Coordinator Dr. Leah Newman



IE 4810  Engineering Accounting 3 lecture hours 0 lab hours 3 credits Course Description An overview of basic topics in financial and managerial accounting as applied to manufacturing companies and engineering projects is presented with a focus on helping engineers understand the meaning of financial metrics and how they are used in business planning and decision making. The basics of financial and managerial accounting are taught along with inventory valuation and depreciation methods in the context of the engineering business environment. The focus of the course is on information for cost management, budgetary control, and shortterm and longterm financial decision making. Students will work in teams to prepare an indepth financial analysis of an engineering project. (prereq: BA 2510 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand the underlying concepts of financial and managerial accounting and the roles of profit, cash, and cost in decision making related to engineering projects
 Allocate overhead in manufacturing, service, and other engineeringoriented organizations
 Explain the underlying concepts of accounting as they apply to manufacturing enterprises and the specific roles that depreciation, inventory valuation, and time offsetting play in their operations
 Knowledgeably use managerial accounting information for decisionmaking in manufacturing and engineering related businesses
 Create cost estimates for engineering projects and properly price engineering services
 Accumulate and record financial data in accordance with Generally Accepted Accounting Principles (GAAP) applied to engineeringrelated businesses and projects
 Recognize and understand some of the ethical and legal issues related to the practice of accounting
Prerequisites by Topic
 Introduction to accounting
Course Topics
 Role of accounting in engineering
 Ledgers and transactions
 Equity, assets, and liabilities
 Balance sheets, income statements
 Project expensing, accounting cycle for projects
 Adjusting financial statements, cash flow analysis
 Engineering payroll considerations, managerial accounting
 Revenue reporting, receivables and collections, operating income
 Cost drivers and estimation, product costing and pricing
 Inventory allocation, writing down inventory, inventory disclosures
 Income taxes, operational and capital budgeting, earned value analysis
 Planning, organizing, and control cycle
 Ethical and corporate responsibility
 Performance reporting and scorecards
Coordinator Dr. Aaron Armstrong



IE 4823  Financial Engineering 3 lecture hours 0 lab hours 3 credits Course Description Finance and economic analysis is a growing area of employment for engineers. The purpose of this class is to increase the student’s ability to apply engineering methods to finance, insurance, economics, and risk management. This is a studentdirected course, where the interests of the participating students will influence the content and objectives of the course. Student influenced course topics may include but are not necessarily limited to options pricing theory, futures contracts and other financial instruments, real options, risk management, and game theory. Industry applications and case studies illustrate concepts and challenges. (prereq: IE 423 and junior standing) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand how engineering methods apply to finance, insurance, and economics
 Understand options, futures, and other financial instruments
 Understand real options
 Understand risk and how risk is evaluated and incorporated into financial models
 Apply game theory concepts to financial analysis
Prerequisites by Topic Course Topics
 Introduction to financial engineering
 Application of engineering methods to finance, insurance, and economics
 Mathematical modeling for financial analysis and decision making
 Options, futures, and other financial instruments
 Real options
 Evaluating risk and incorporating it into financial models
 Game theory
 Industry applications and case studies
Coordinator Dr. Aaron Armstrong



IE 4880  Supply Chain Engineering 3 lecture hours 0 lab hours 3 credits Course Description Supply chain design and logistical planning and execution are critical areas for many businesses and industries. This class is intended to increase students’ understanding of how to apply engineering methods to supply chain related problems. Student influenced course topics may include but are not necessarily limited to: supply chain demand modeling, multitier forecasting and coordination, negotiation strategies, total acquisition cost calculation, make versus buy decision analysis, integration of supply chain with product development, dynamic lot sizing inventory models, and the bullwhip effect. Industry applications and case studies illustrate concepts and challenges. (prereq: junior standing) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand how engineering methods apply to supply chain problems
 Model a dynamic supply chain
 Forecast demand and incorporate this forecast across the supply chain model
 Complete a capacity planning analysis
 Understand negotiation strategies and where to apply them
 Explain software tools and methods available for logistical network design and operation
 Understand make versus buy decisions and the associated cost analysis
 Understand the bullwhip effect and how it can be dampened
Prerequisites by Topic Course Topics
 Introduction to supply chain engineering
 Operations research models for supply chain analysis
 Forecasting
 Capacity planning
 Negotiation strategies
 Software tools and methods for logistics network design
 Make versus buy decisions
 Bullwhip effect and other flow problems
 Integration problems in supply chain management
 Industry applications and case studies
Coordinator Dr. Aaron Armstrong



IE 4901  Industrial Engineering Senior Design Project I 2 lecture hours 2 lab hours 3 credits Course Description This is the first of a two (three) course sequence in developing and executing a team capstone design project in Industrial Engineering. The purpose of this project is to demonstrate the student’s ability, working within a design team, to integrate the knowledge, skills, and experiences acquired in the Industrial Engineering program. Evaluation of user (client) needs, development of an engineering specification, appropriate evaluation criteria, and techniques for design in the presence of conflicting design constraints (quality, productivity, safety, cost) are reviewed. This course includes an external clientsponsored design project and a design proposal submitted to, and approved by, the client. (prereq: senior standing, GS 1003 , IE 391 , IE 440, consent of instructor) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand the client’s situation and define the problem/opportunity with a clear and concise project purpose and scope
 Utilize input from the client to establish performance improvement objectives
 Define an appropriate solution methodology, collect relevant data and information, and identify relevant analytical methods and tools
 Create a detailed and executable project schedule
 Utilize agendas and minutes to plan for and document the results of client meetings
 Communicate, verbally and in writing, the project proposal and project plan
 Function as an effective team member in the context of a realworld project
Prerequisites by Topic
 Must have sufficient knowledge of specific industrial engineering techniques that are likely to relate to the course project (such as operations research, manufacturing systems analysis, lean manufacturing, production control, ergonomics, safety, etc.).
 Must have successfully completed the junior project class, demonstrating the student’s ability to work successfully within a team on a clientsponsored industrial engineering project
Course Topics
 Project proposals
 Teamwork, performance evaluations, peer feedback
 Formal presentations
 Project schedules
 Literature review and library research
 Data gathering and analysis
 Formal technical reports
Laboratory Topics
 All laboratory work will be done at the sponsor site or in an MSOE lab, as needed by a particular project
Coordinator Dr. Leah Newman



IE 4902  Industrial Engineering Senior Design Project II 1 lecture hours 3 lab hours 3 credits Course Description In this second of the senior design courses, the student teams execute the design proposal developed in IE 4901 . The design is documented in a written team report and orally defended before a faculty review panel. Typically, the project is also presented to the client in a separate presentation, often at the client facility. (prereq: IE 4901 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Utilize relevant industrial engineering methods and tools to collect and analyze data
 Formulate creative alternatives, perform systematic comparisons of alternatives, and formulate recommendations based on quantitative and qualitative evaluations
 Justify recommendations based on quantitative and qualitative performance metrics, taking the context of the client organization into consideration
 Communicate, verbally and in writing, the project methodology, results, recommendations, and organizational impact
 Write an abstract that is clear and concise, emphasizing the most important aspects of the project and its potential for impact at the client organization
 Develop a poster that creates interest and clearly highlights key aspects of the project
Prerequisites by Topic
 Must have developed a clientapproved project proposal in IE 4901
Course Topics
 Topics are geared towards helping the students satisfactorily complete their projects
 Topics may vary depending on the content of the projects and the specific strengths and weaknesses of the students enrolled in the course
 Topics covered could include review of technical information or techniques, technical writing, and effective oral presentations
Laboratory Topics
 All laboratory work will be done at the sponsor site or in an MSOE lab, as needed by a particular project
Coordinator Dr. Leah Newman



IE 4903  Industrial Engineering Senior Design Project III 1 lecture hours 3 lab hours 3 credits Course Description This course provides a mechanism for a design team, with approval received during IE 4901 from the course coordinator and faculty advisor, to undertake a larger scope project with correspondingly longer planned duration. A final project presentation and written report are submitted at the end of IE 4902 . IE 4903 is subsequently handled in a similar fashion as an independent study course, with the deliverables and expectations set by the faculty advisor. The additional time may be used for building a prototype of the design, implementing changes within a company, or other means of expanding the project scope. A grade is given just for the IE 4903 deliverables; there is no carryover from IE 4902 . This course satisfies the requirements of an Industrial Engineering elective. (prereq: IE 4902 , consent of instructor) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Depends on project objectives and scope
Prerequisites by Topic
 Must have developed a clientapproved project proposal in IE 4901 and been given consent by the course coordinator and faculty advisor to undertake a larger scope project
Course Topics
 This course is administered similarly to an independent study course. Students meet weekly with their advisor to discuss project progress and concerns
 Topics covered in weekly meetings are geared towards helping the students satisfactorily complete their projects
 Topics may vary depending on the content of the projects and the specific strengths and weaknesses of the students enrolled in the course
 Topics covered could include review of technical information or techniques, technical writing, and effective oral presentations
Laboratory Topics
 All laboratory work will be done at the sponsor site or in an MSOE lab, as needed by a particular project
Coordinator Dr. Leah Newman

Mathematics 


MA 120  Precalculus Mathematics 4 lecture hours 0 lab hours 4 credits Course Description This course provides a review of the aspects of algebra, trigonometry, and analytic geometry that are necessary for success in calculus for the benefit of students with slight deficiencies in any of these areas. It is not intended as a substitute for a rigorous course in any of these topics. (prereq: MA 125 or equivalent) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Be proficient with exponential expressions including the laws of exponents and negative and rational exponents
 Factor simple polynomial expressions
 Simplify rational expressions including products, sums, and complex rational expressions
 Solve rational equations including consideration of the domain by means of linear approach and quadratic approach (solving by factoring and/or quadratric formula)
 Be able simplify radical expressions including rationalizing the numerator or denominator
 Solve radical equations (optional)
 Understand the concept of a function, its range and domain, and its graph
 Be proficient with linear functions and models including recognizing that the slope represents rate of change
 Know the graphs of common equations
 Transform the graphs of functions graphically and algebraically
 Understand piecewisedefined functions
 Use operations of functions including composition of functions on calculus concepts such as difference quotients
 Understand exponential functions, their domain and range, and graphs
 Understand logarithmic functions, their domain and range, and graphs
 Be proficient with the properties of logarithms including solving exponential equations
 Understand the measure of an angle including radians and degrees
 Understand the definition of the six trigonometric functions including their relation to the geometry of the unit circle and right triangles
 Evaluate the trigonometric functions both approximately, by using the calculator, and exactly, by using reference angles of common angles
 Apply trigonometric properties to applications
 Know the graphs of the three of sine, cosine and tangent. Recognize the remaining three trigonometric graphs
 Be proficient with basic trigonometric identities including reciprocal identities, ratio identities and Pythagorean identities
 Be familiar with other trigonometric identities (double angle and reduction or halfangle identities)
 Understand the definition of the inverse trigonometric functions and be able to evaluate to find common angles
 Solve basic trigonometric equations
Prerequisites by Topic Course Topics
 Inequalities and absolute value
 Factoring and completing the square
 General functions
 Rational functions
 Trigonometric functions
 Exponential and log functions
 Complex numbers
 Systems of equations
Coordinator Dr. Anthony van Groningen



MA 120A  Precalculus Mathematics 4 lecture hours 1 lab hours 4 credits Course Description This course is the same as MA 120 . The ‘A’ designation after the course number indicates this is a special section taught with extra math lab hours built in as a requirement for successful completion of the course. (prereq: MA 125 or equivalent and consent of instructor) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Be proficient with exponential expressions including the laws of exponents and negative and rational exponents
 Factor simple polynomial expressions
 Simplify rational expressions including products, sums, and complex rational expressions
 Solve rational equations including consideration of the domain by means of linear approach and quadratic approach (solving by factoring and/or quadratric formula)
 Be able simplify radical expressions including rationalizing the numerator or denominator
 Solve radical equations (optional)
 Understand the concept of a function, its range and domain, and its graph
 Be proficient with linear functions and models including recognizing that the slope represents rate of change
 Know the graphs of common equations
 Transform the graphs of functions graphically and algebraically
 Understand piecewisedefined functions
 Use operations of functions including composition of functions on calculus concepts such as difference quotients
 Understand exponential functions, their domain and range, and graphs
 Understand logarithmic functions, their domain and range, and graphs
 Be proficient with the properties of logarithms including solving exponential equations
 Understand the measure of an angle including radians and degrees
 Understand the definition of the six trigonometric functions including their relation to the geometry of the unit circle and right triangles
 Evaluate the trigonometric functions both approximately, by using the calculator, and exactly, by using reference angles of common angles
 Apply trigonometric properties to applications
 Know the graphs of the three of sine, cosine and tangent. Recognize the remaining three trigonometric graphs
 Be proficient with basic trigonometric identities including reciprocal identities, ratio identities and Pythagorean identities
 Be familiar with other trigonometric identities (double angle and reduction or halfangle identities)
 Understand the definition of the inverse trigonometric functions and be able to evaluate to find common angles
 Solve basic trigonometric equations
Prerequisites by Topic Course Topics
 Properties of exponents
 Factoring and simplifying polynomials, rational and radical expressions
 Solving quadratics and trigonometric functions
 General functions and their properties: linear, basic graphs, piecewise
 Trigonometric functions and their properties
 Exponential and log functions
Coordinator Dr. Anthony van Groningen



MA 125  College Algebra I 4 lecture hours 0 lab hours 4 credits Course Description This course provides a review of basic algebra. Topics covered include fundamental algebraic operations; equations, ratio and proportion, variation; systems of linear equations; factoring and fractions; quadratic equations. (prereq: none) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Perform the four fundamental operations with signed numbers and polynomials
 Remove and insert symbols of grouping
 Perform basic operations with exponents and radicals
 Solve systems of two equations in two unknowns
 Find special products
 Factor polynomials
 Reduce a given fraction to lowest terms
 Perform the four fundamental operations with fractions
 Simplify complex fractions
 Solve fractional equations
 Solve quadratic equations
 Solve word problems leading to algebraic equations
Prerequisites by Topic Course Topics
 Fundamental operations
 Equations and applications
 Systems of equations
 Special products and factoring
 Operations with algebraic fractions
 Quadratic equations
Coordinator Dr. Anthony van Groningen



MA 125A  College Algebra I 4 lecture hours 1 lab hours 4 credits Course Description This course is the same as MA 125. The ‘A’ designation after the course number indicates this is a special section taught with extra math lab hours built in as a requirement for successful completion if the course. (prereq: consent of instructor) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Perform the four fundamental operations with signed numbers and polynomials
 Remove and insert symbols of grouping
 Perform basic operations with exponents and radicals
 Solve systems of two equations in two unknowns
 Find special products
 Factor polynomials
 Reduce a given fraction to lowest terms
 Perform the four fundamental operations with fractions
 Simplify complex fractions
 Solve fractional equations
 Solve quadratic equations
 Solve word problems leading to algebraic equations
Prerequisites by Topic Course Topics
 Fundamental operations
 Equations and applications
 Systems of equations
 Special products and factoring
 Operations with algebraic fractions
 Quadratic equations
Coordinator Dr. Anthony van Groningen



MA 126  Trigonometry 4 lecture hours 0 lab hours 4 credits Course Description Topics include trigonometric functions, special angles, solution of triangles, radian measure, graphs, inverse trigonometric functions, solution of trigonometric equations, basic identities and the sum, difference, double angle and half angle formulas. An introduction to exponents and logarithms is included. (prereq: MA 125 or equivalent) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Define the six trigonometric functions
 Determine the smallest positive angle coterminal with a given angle
 Use a calculator to find the values of trigonometric functions and inverse trigonometric functions
 Determine the values of trigonometric functions of quadrantal and special angles
 Solve triangles
 Convert from degree measure to radian measure and vice versa
 Find the length of a circular arc and the area of a circular sector
 Graph sine and cosine functions
 Evaluate inverse trigonometric functions
 Prove trigonometric identities using fundamental relationships
 Prove trigonometric identities using sum, difference, doubleangle, and halfangle formulas
 Solve trigonometric equations
 Use properties of logarithms
 Solve exponential equations by means of logarithms
Prerequisites by Topic
 Fundamental algebraic operations
 Equations and systems of equations
 Special products and factoring
 Operations with algebraic fractions
 Quadratic equations
Course Topics
 Trigonometric functions and right triangle applications
 Laws of sines and cosines
 The unit circle
 Trigonometric functions and real numbers
 Addition and subtraction formulas
 Double and half angles formulas
 Trigonometric graphs
 Basic trigonometric identities
 Inverse trigonometric functions
 Trigonometric equations
 Exponential functions
 Logarithmic functions
 Laws of logarithms
 Exponential and logarithmic equations
 Applications of exponential and logarithmic equations
Coordinator Dr. Kseniya Fuhrman



MA 127  College Algebra II 4 lecture hours 0 lab hours 4 credits Course Description This course provides a review or introduction to more advanced algebra. Topics covered include exponents and radicals; solving linear, quadratic and selected radical and polynomial equations; an introduction to analytic geometry; the function concept and terminology; determinants, matrices and systems of linear equations; the binomial theorem. (prereq: MA 125 or equivalent) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Simplify expressions containing exponents and radicals
 Perform the four fundamental operations with radicals
 Represent complex numbers as vectors
 Perform the four fundamental operations with complex numbers in rectangular form
 Solve linear and quadratic equations
 Convert a given complex number from rectangular to polar form and vice versa
 Multiply and divide complex numbers in polar form
 Use De Moivre’s formula to find powers and roots of complex numbers
 Solve systems of quadratic equations algebraically
 Solve radical equations and equations in quadratic form
 Use synthetic division to find roots of polynomial equations
 Use the properties of determinants to evaluate a determinant of arbitrary order
 Solve linear systems by Cramer’s Rule
 Perform algebraic operations with matrices
 Use row operations to find the inverse of a given matrix and solve a given system of equations
 Use the binomial theorem to expand a given binomial
 Use the distance and midpoint formulas
 Find equations of lines
Prerequisites by Topic
 Fundamental algebraic operations
 Special products and factoring
 Operations with algebraic functions
 Quadratic equations
 Basic concepts of trigonometry
Course Topics
 Exponents and radicals
 Algebraic expressions
 Factoring
 Linear equations and applications
 Quadratic equations
 Other equations
 Coordinate plane
 Functions
 Systems of linear equations
 Matrices
 Determinants
 Binomial Theorem
Coordinator Dr. Kseniya Fuhrman





MA 136  Calculus I 4 lecture hours 0 lab hours 4 credits Course Description This course begins with a short review of topics in algebra and trigonometry before introducing the student to differential calculus. Topics include algebra of functions, limits, continuity, differentiation of algebraic, trigonometric, exponential and logarithmic functions and application of the derivative to curve sketching and optimization problems. (prereq: MA 120 or equivalent) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand the Mean Value Theorem
 Evaluate the limits of algebraic, trigonometric, exponential and logarithmic functions
 Identify removable and nonremovable discontinuities
 Evaluate the derivative of algebraic, trigonometric, exponential and logarithmic functions
 Find the equation of a tangent line to a curve
 Find the position, velocity and acceleration of a moving object
 Use derivatives to find relative extrema and points of inflection on a curve
 Set up and solve optimization problems
 Set up and solve related rate problems
Prerequisites by Topic
 Simplification of algebraic expressions containing complex fractions, exponents, and radicals
 Factoring
 Linear, fractional, and quadratic equations
 Cartesian coordinate system
 Systems of equations
 Trigonometric functions
 Trigonometric identities
Course Topics
 Algebra and trigonometry review
 Functions
 Limits and continuity
 Rates of change, tangent lines, and definition of derivative
 Derivatives of algebraic and trigonometric functions
 Derivatives of exponential and logarithmic and inverse trig functions
 First and second derivative tests for extrema, curve sketching
 Applied optimization problems
 Related rates problems
 Mean Value Theorem
Coordinator Dr. Anthony van Groningen



MA 136A  Calculus I 4 lecture hours 1 lab hours 4 credits Course Description This course is the same as MA 136 . The ‘A’ designation after the course number indicates there are extra math lab hours built in as a requirement for successful completion of the course. (prereq: MA 120 or equivalent and consent of instructor) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand the Mean Value Theorem
 Evaluate the limits of algebraic, trigonometric, exponential and logarithmic functions
 Identify removable and nonremovable discontinuities
 Evaluate the derivative of algebraic, trigonometric, exponential and logarithmic functions
 Find the equation of a tangent line to a curve
 Find the position, velocity and acceleration of a moving object
 Use derivatives to find relative extrema and points of inflection on a curve
 Set up and solve optimization problems
 Set up and solve related rate problems
Prerequisites by Topic
 Simplification of algebraic expressions containing complex fractions, exponents, and radicals
 Factoring
 Linear, fractional, and quadratic equations
 Cartesian coordinate system
 Systems of equations
 Trigonometric functions
 Trigonometric identities
Course Topics
 Algebra and trigonometry review
 Functions
 Limits and continuity
 Rates of change, tangent lines, and definition of derivative
 Derivatives of algebraic and trigonometric functions
 Derivatives of exponential and logarithmic and inverse trig functions
 First and second derivative tests for extrema, curve sketching
 Applied optimization problems
 Related rates problems
 Mean Value Theorem
Coordinator Dr. Anthony van Groningen



MA 136C  Calculus I 4 lecture hours 0 lab hours 4 credits Course Description This course is the same as MA 136. The ‘C’ designation after the course number indicates enrollment in Carter Academy. (prereq: enrollment in Carter Academy and consent of instructor) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand the Mean Value Theorem
 Evaluate the limits of algebraic, trigonometric, exponential and logarithmic functions
 Identify removable and nonremovable discontinuities
 Evaluate the derivative of algebraic, trigonometric, exponential and logarithmic functions
 Find the equation of a tangent line to a curve
 Find the position, velocity and acceleration of a moving object
 Use derivatives to find relative extrema and points of inflection on a curve
 Set up and solve optimization problems
 Set up and solve related rate problems
Prerequisites by Topic
 Simplification of algebraic expressions containing complex fractions, exponents, and radicals
 Factoring
 Linear, fractional, and quadratic equations
 Cartesian coordinate system
 Systems of equations
 Trigonometric functions
 Trigonometric identities
Course Topics
 Algebra and trigonometry review
 Functions
 Limits and continuity
 Rates of change, tangent lines, and definition of derivative
 Derivatives of algebraic and trigonometric functions
 Derivatives of exponential and logarithmic and inverse trig functions
 First and second derivative tests for extrema, curve sketching
 Applied optimization problems
 Related rates problems
 Mean Value Theorem
Coordinator Dr. Anthony van Groningen



MA 137  Calculus II 4 lecture hours 0 lab hours 4 credits Course Description This course is a continuation of MA 136 and an introduction to integral calculus. Topics include Newton’s method, differentials, basic integrals involving algebraic, trigonometric, exponential, logarithmic, and inverse trig functions. Topics also include rectilinear motion, work, areas and volumes of revolution, integration techniques such as integration by parts and partial fractions, and numerical integration methods. (prereq: MA 136) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Use Newton’s method to approximate the zeros of a function
 Find the differential of a function and use it to approximate error
 Integrate algebraic, exponential, trigonometric, logarithmic and inverse trigonometric functions
 Evaluate a definite integral by the limit of Riemann sums
 Evaluate a definite integral by Fundamental Theorem of Calculus
 Use method of substitution to find indefinite and definite integrals
 Use method of integration by parts
 Integrate products and powers of trigonometric functions
 Integrate functions using partial fractions
 Find areas between curves
 Find volumes of solids of revolution using disk and washer methods
 Use integration to solve work problems
Prerequisites by Topic
 Graphing of functions
 Derivatives of algebraic, exponential, trigonometric, inverse trig and logarithmic functions
 Limits of algebraic and trigonometric functions
 Implicit derivatives
 Graphing using relative extrema
Course Topics
 Newton’s method of approximating zeros of a function
 Differentials
 Area problem and indefinite integrals
 The definite integral as the limit of Riemann sums and the Fundamental Theorem of Calculus
 Integration by substitution
 Areas between curves
 Rectilinear motion
 Volumes by disk and washers
 Work
 Integration by parts
 Integration of products and powers of trig functions
 Integration using partial fractions
 Integration using trigonometric substitutions (optional)
 Numerical integration
Coordinator Dr. Kseniya Fuhrman



MA 137A  Calculus II 4 lecture hours 1 lab hours 4 credits Course Description This course is the same as MA 137 . The ‘A’ designation after the course number indicates there are extra math lab hours built in as a requirement for successful completion of the course. (prereq: MA 136 and consent of instructor) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Use Newton’s method to approximate the zeros of a function
 Find the differential of a function and use it to approximate error
 Integrate algebraic, exponential, trigonometric, logarithmic, and inverse trigonometric functions
 Evaluate a definite integral by the limit of Riemann sums
 Evaluate a definite integral by Fundamental Theorem of Calculus
 Use method of substitution to find indefinite and definite integrals
 Use method of integration by parts
 Integrate products and powers of trigonometric functions
 Integrate functions using partial fractions
 Find areas between curves
 Find volumes of solids of revolution using disk and washer methods
 Use integration to solve work problems
Prerequisites by Topic
 Graphing of functions
 Derivatives of algebraic, exponential, trigonometric, inverse trig, and logarithmic functions
 Limits of algebraic and trigonometric functions
 Implicit derivatives
 Graphing using relative extrema
Course Topics
 Newton’s method of approximating zeros of a function
 Differentials
 Area problem and indefinite integrals
 The definite integral as the limit of Riemann sums and the Fundamental Theorem of Calculus
 Integration by substitution
 Areas between curves
 Rectilinear motion
 Volumes by disk and washers
 Work
 Integration by parts
 Integration of products and powers of trig functions
 Integration using partial fractions
 Integration using trigonometric substitutions (optional)
 Numerical integration
Coordinator Dr. Kseniya Fuhrman



MA 231  Calculus III 4 lecture hours 0 lab hours 4 credits Course Description This course is a continuation of MA 137 and an introduction to multivariable calculus. Topics include L’Hȏpital’s rule, improper integrals, applications of integrals to physics, parametric equations, polar coordinates, vector algebra, surfaces in three dimensions, and partial derivatives with applications. (prereq: MA 137 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Use L’Hȏpital’s Rule to evaluate a limit
 Evaluate improper integrals
 Find the length of the arc of a curve
 Find work, fluid pressure, and force
 Eliminate the parameter from parametric equations
 Draw graphs of parametric equations and determine the direction of travel for an increasing parameter
 Find first and second derivatives of parametric functions
 Find the arc length for parametric curves
 Convert between rectangular and polar coordinates
 Draw graphs of polar curves
 Find area and arc length in polar coordinates
 Perform operations using vector algebra
 Find dot products, cross products, and equations of lines and planes in three dimensions
 Sketch surfaces in three dimensions
 Find first and second partial derivatives
 Find the total differential of a function of more than one variable and use it to approximate error
 Use chain rules to find derivatives and partial derivatives
 Find implicit partial derivatives
 Determine the maximum, minimum, and saddle points on a surface
Prerequisites by Topic
 The basic principles of algebra
 The basic principles of trigonometry
 Differentiation and integration of algebraic and transcendental functions
 Limits
 Understanding of the definition of the definite integral
Course Topics
 L’Hȏpital’s Rule
 Improper integrals
 Arc length
 Work
 Fluid pressure and force
 Parametric equations
 Polar coordinates and graphs
 Vectors, lines, and planes
 Surfaces in three dimensions
 Functions of several variables
 Partial derivatives
 Extrema of functions of two variables
Coordinator Dr. Chunping Xie



MA 232  Calculus IV 3 lecture hours 0 lab hours 3 credits Course Description This course is a continuation of MA 231 and an introduction to multiple integration and infinite series. Topics include double and triple integrals with applications to areas, volumes and moments, infinite series with tests for convergence, power series, Taylor and Maclaurin series, and operations with series. (prereq: MA 231 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Set up and evaluate double integrals using rectangular and polar coordinates
 Find areas, volumes, and moments using double integrals
 Set up and evaluate triple integrals
 Use triple integrals to find volumes and moments of solids
 Use integrals in cylindrical or spherical coordinates to find volumes and moments
 Test sequences for convergence and divergence
 Test infinite series for convergence and divergence
 Find the interval of convergence for a power series
 Perform algebraic and calculus operations on power series
 Use Taylor and Maclaurin series to approximate functions
Prerequisites by Topic
 The basic principles of algebra
 The basic principles of trigonometry
 Differentiation and integration of algebraic and transcendental functions
 Applications of integration
 Integration techniques
 L’Hȏpital’s Rule
 Functions of several variables
 Partial derivatives
 Limits and improper integrals
 Parametric equations
 Polar coordinates
Course Topics
 Double integrals, area, volume, and moments
 Triple integrals, volume, moments, cylindrical and spherical coordinates
 Sequences
 Infinite series and tests for convergence
 Power series and intervals of convergence
 Taylor and Maclaurin series
Coordinator Edward Griggs



MA 235  Differential Equations 4 lecture hours 0 lab hours 4 credits Course Description This course discusses the solution of firstorder differential equations, the solution of higherorder differential equations with constant coefficients, applications of differential equations, and an introduction to the method of Laplace transforms applied to the solution of certain differential equations. (prereq: MA 231 or MA 2314 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Determine the solution of firstorder differential equations by the method of separation of variables
 Determine the solution of firstorder differential equations having homogeneous coefficients
 Determine the solution of exact firstorder differential equations
 Determine appropriate integrating factors for firstorder linear differential equations
 Apply and solve firstorder differential equations of selected physical situations
 Determine the general and particular solutions of higherorder linear homogeneous differential equations with constant coefficients
 Determine the general and particular solutions of certain nonlinear secondorder homogeneous differential equations with constant coefficients using the methods of Undetermined Coefficients and Variation of Parameters
 Apply and solve secondorder differential equations of selected physical situations
 Determine the Laplace transform of selected elementary functions (such as polynomials and exponential and trigonometric functions having linear arguments)
 Determine a function having a given Laplace transform; that is, determine the inverse Laplace transform of a function
 Solve linear differential equation of various orders using the method of Laplace transforms
Prerequisites by Topic
 Determinants
 Solution of algebraic equations
 Limits including L’Hopital’s rule
 Differentiation of algebraic and transcendental functions
 Integration (especially improper and the method of partial fractions)
 Factoring of polynomials
Course Topics
 Basic concepts
 Solution of firstorder differential equations by separation of variables
 Solution of exact equations
 Solution of firstorder linear differential equations
 Solution of firstorder differential equations using numerical methods
 Solution of physical situations that can be modeled by firstorder differential equations
 Solution of higher order homogeneous differential equations with constant coefficients
 Solution of nonhomogeneous higherorder differential equations using the method of Undetermined Coefficients
 Solution of nonhomogeneous higherorder differential equations using the method of Variation of Parameters
 Solution of physical situations that can be modeled by higherorder differential equations
 Introduction of Laplace transforms
 Laplace transforms of elementary functions
 Inverse Laplace transforms
 Solution of linear differential equations with constant coefficients using Laplace transforms
 Applications of Laplace transforms
Coordinator Dr. Chunping Xie



MA 262  Probability and Statistics 3 lecture hours 0 lab hours 3 credits Course Description This course provides a basic introduction to the laws of probability needed to perform statistical analyses. Both descriptive and inferential statistics are considered. Probability distributions, the Central Limit Theorem, confidence intervals, hypothesis testing, and analysis of variance are considered in depth. Note: students cannot receive credit for both MA 262 and MA 3611 . (prereq: MA 137 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Be familiar with the terminology and nomenclature of both probability and statistics
 Know the difference between a parameter and a statistic
 Know the difference between a population and a sample
 Understand the basic concepts and properties of probability
 Understand the meaning and significance of the standard deviation
 Calculate the mean and variance of probability distributions
 Be familiar with, and able to calculate probabilities of, the binomial, Poisson, Normal, Studentt, Chisquare, and F distributions
 Construct appropriate confidence intervals for population parameters
 Have a basic familiarity with the Central Limit Theorem and realize that it affects the calculations of test values and confidence intervals
 Perform hypothesis tests concerning the means, variances, and proportions of one or two populations
 Perform hypothesis tests concerning the comparison of means of more than two populations
Prerequisites by Topic
 Algebra
 Trigonometry
 Differentiation of algebraic and transcendental functions
 Integration of algebraic and transcendental functions
Course Topics
 Measures of central tendency and dispersion
 Introduction to probability and the laws of probability
 Discrete probability distributions: binomial and Poisson
 Introduction to the Central Limit Theorem
 Continuous probability distributions: normal, t, chisquare, and F
 Onesample hypothesis testing and statistical inference
 Onesample confidence intervals and statistical inference
 Twosample confidence intervals and statistical inference
 Twosample hypothesis testing and statistical inference
 Analysis of variance
Coordinator Dr. Won Chul Song



MA 315  Nursing Statistics 3 lecture hours 0 lab hours 3 credits Course Description This course considers both visual and calculational aspects of statistics. The major portion of the course deals with the analysis of data, including medical data. Calculational topics include the estimation of population parameters, tests of hypotheses, and tests for goodness of fit. Note: this course is open only to students in the School of Nursing. (prereq: MA 125 or MA 1204 or equivalent) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand basic statistical terminology
 Produce data through sampling and experimental design
 Classify data by type
 Produce several methods of visually displaying data
 Compute measures of central tendency and measures of dispersion
 Understand the meaning, calculation, and interpretation of linear regression and correlation results
 Have a basic understanding of the normal distribution and its application to appropriate statistical situations
 Have a basic understanding of the concepts of sampling error and sampling distributions
 Have an understanding as to the construction of confidence intervals for the population mean and the importance of the Studentt distribution to the construction of such confidence intervals
 Have an understanding concerning the performance of hypothesis tests for the mean of a single population
 Have an understanding relating to inferences for the comparison of two population means
 Have a basic understanding with respect to the use of the chisquare distribution in goodnessoffit and tests for independence calculations
Prerequisites by Topic
 Simplification of algebraic expressions containing fractions, exponents and radicals
 Factoring
 Linear and quadratic equations
 Cartesian coordinate system
 Systems of equations
Course Topics
 Descriptive and inferential statistics introduction and discussion
 Linear regression
 The normal distribution and its use in statistics
 The central limit theorem and its importance to statistics
 Confidence intervals for the population mean
 Types of statistical errors
 Hypothesis testing
 Chisquare situations
 Analysis of variance
Coordinator Edward Griggs



MA 327  Mathematical Modeling 4 lecture hours 0 lab hours 4 credits Course Description The construction of a mathematical model requires the modeler to describe physical characteristics and processes of behavior in physical and natural systems by invoking mathematical language, using mathematical laws and concepts. Then the model is used to verify known results of the past and present, and to hopefully be able to extrapolate the future events. Topics of the course might include, depending upon instructor and student interest, statistical models, differential equations models, difference equations, Markov processes, optimization, etc. (prereq: MA 235 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
Prerequisites by Topic
 Calculus (single and multivariable)
 Differential equations
 Probability and statistics
Coordinator Dr. Chunping Xie



MA 330  Vector Analysis 3 lecture hours 0 lab hours 3 credits Course Description This course provides a brief study of vector algebra and vector calculus, including velocity and acceleration, space curves, gradient, divergence and curl using the del operator, line, surface and volume integrals, conservative fields, curvilinear coordinates, Green’s theorem, the divergence theorem, and Stokes’ theorem. (prereq: MA 232 or MA 2323 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Perform elementary vector operations
 Find the equations of lines and planes
 Differentiate vector functions of one variable
 Analyze threedimensional curves
 Calculate the divergence and curl of a vector field
 Calculate line, surface, and volume integrals
 Use the divergence and Stokes’ theorems to facilitate integral calculation
Prerequisites by Topic
 Basic vector algebra
 Three dimensional analytic geometry
 Differential and integral calculus
Course Topics
 Vector valued functions
 Vector fields
 Line integrals
 Green, Gauss and Stokes’ theorems
Coordinator Dr. Anthony van Groningen



MA 340  Business Statistics 4 lecture hours 0 lab hours 4 credits Course Description Almost all managerial decisions involve some amount of uncertainty. This course is designed to acquaint the student with some of the statistical methods that can be used to help make these decisions. Topics covered are probability, probability models, estimation, tests of hypotheses, analysis of variance, and regression. Note: This course is open only to students in the Rader School of Business. (prereq: MA 120 or equivalent) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Set up a frequency distribution
 Compute the mean and standard deviation of a set of numbers
 Determine probabilities of specific events
 Recognize and use the binomial and normal probability distributions
 Test a hypothesis about means and the binomial parameter ‘p’
 Estimate the mean of a population and the parameter ‘p’
 Understand analysis of variance and be able to calculate linear and multiple regression using Microsoft® Excel*. *Microsoft is a registered trademark of Microsoft Corporation in the United States and/or other countries
Prerequisites by Topic Course Topics
 Introduction
 Probability
 Discrete probability distributions
 Binomial distribution
 Poisson distribution
 Hypergeometric distribution
 Continuous probability distributions
 Normal distribution
 Exponential distribution
 Sampling
 Hypothesis testing
 Estimating mean and variance
 Estimating proportion
 Analysis of variance
 Regression
Coordinator Edward Griggs



MA 343  Linear Programming 3 lecture hours 0 lab hours 3 credits Course Description This course introduces the fundamentals of linear programming methods. Topics include formulating reallife problems (such as production planning, inventory, shortest path, and assignment problems) as linear programs, the simplex algorithm, geometry of feasible regions and optimal solutions, duality theory, and complementary slackness conditions. Tools relating linear and integer programs such as Gomory cuts and branchandbound methods will also be introduced. (prereq: MA 2314 or MA 231 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Formulate various reallife problems as linear and integer programs
 Solve linear programs using the simplex algorithm
 Construct the dual problem of a general linear program
 Understand the weak and strong duality relations for linear programs
 Verify optimal solutions using duality theory and complementary slackness conditions
 Execute the primaldual algorithm to solve the shortest path problem
 Tackle integer programs using linear programming tools such as Gomory cuts and branchandbound methods
Prerequisites by Topic
 College algebra including basic operations and concepts with real vectors and matrices
Course Topics
 Linear algebra review
 Formulation of linear and integer programs
 Outcomes of linear programs
 Basic feasible solutions and the simplex method
 The 2phase method
 Duality theory and complementary slackness
 Primaldual algorithm  shortest path problem
 Gomory cuts and branchandbound
Coordinator Edward Griggs



MA 344  Nonlinear Programming 3 lecture hours 0 lab hours 3 credits Course Description This course introduces the fundamentals of nonlinear optimization. Topics include convex sets and functions, necessary and sufficient optimality conditions, duality in convex optimization, and algorithms for unconstrained and constrained optimization problems. (prereq: MA 343 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand the differences between linear, integer and nonlinear programs, as well as their levels of computational complexities
 Learn the basic properties of convex sets and functions and common operations that preserve convexity
 Solve small constrained and unconstrained convex nonlinear programs by hand
 Understand and be able to verify the KarushKuhnTucker optimality conditions
 Understand the Lagrangian function, and the notion of duality in convex optimization
Prerequisites by Topic
 The basic principles of algebra
 Differentiation of algebraic functions
 Exposure to multivariate calculus and partial derivatives
 Experience with formulating industrial and graph theoretical problems using integer and linear programs
 Duality theory in linear programming
 Exposure to vectors and matrices
Course Topics
 Introduction to nonlinear programs
 Convex sets and functions
 KarushKuhnTucker conditions, gradient version
 Lagrangian duality
 Algorithms for unconstrained optimization
 Algorithms for constrained optimization
Coordinator Edward Griggs



MA 380  Advanced Differential Equations 3 lecture hours 0 lab hours 3 credits Course Description This course presents the student with more powerful methods of solving differential equations. Topics include matrix methods for solution of systems of linear differential equations, openform solutions of linear differential equations with variable coefficients using infinite series (including the method of Frobenius), and additional Laplace transform methods. (prereq: MA 235 , and MA 232 or MA 2323 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Solve some linear systems of ordinary differential equations by Laplace transforms and differential operator methods including the Heaviside function, convolutions, Gamma functions, and periodic functions
 Solve some linear ordinary differential equations with variable coefficients near an ordinary point
 Solve some linear ordinary differential equations with variable coefficients near a regular singular point
 Solve systems of linear differential equations using matrix methods
Prerequisites by Topic
 Convergence status and interval of convergence of infinite series
 Power series manipulations using differentiation and integration
 Using Maclaurin and Taylor series to approximate functions
 Solution of higherorder linear homogeneous differential equations having constant coefficients
 Solution of nonhomogeneous linear differential equations having constant coefficients using the methods of undetermined coefficients and variation of parameters
 Solution of linear differential equations using Laplace transforms
 Matrix operations such as row manipulations, matrix inversion, and solution of a system of equations using matrices
Course Topics
 Solution of differential equations using Laplace transforms
 Solution of linear differential equations near ordinary points and regular singular points
 Solution of systems of differential equations using matrix methods
Coordinator Dr. Chunping Xie



MA 381  Complex Variables 3 lecture hours 0 lab hours 3 credits Course Description This course is an introduction to the theory of analytic functions of a complex variable. Topics covered include algebra of complex numbers, mapping by elementary functions, analytic functions, complex integrals, Cauchy’s Theorem, power series, Laurent series, residues and poles. (prereq: MA 235 , and MA 232 or MA 2323 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Determine if a complexvalued function is analytic
 Apply the CauchyRiemann equations, Cauchy’s theorem, Cauchy’s integral formula, Cauchy’s inequality, Liouville’s theorem, and the maximum modulus principle to complex valued functions
 Apply Taylor’s theorem, Laurent’s theorem, and residue theorem
Prerequisites by Topic
 Differential and integral calculus
 Elementary differential equations
Course Topics
 Complex numbers and the complex plane
 Analytic functions
 The elementary functions
 Elementary transcendental functions over the complex numbers
 Integration of analytic functions
 Infinite series expansions, residues, and poles
Coordinator Edward Griggs



MA 382  Laplace and Fourier Transforms 3 lecture hours 0 lab hours 3 credits Course Description This course introduces the theoretical concepts and uses of the Laplace and Fourier transforms. It includes Laplace transform of special functions, properties, operations and using Laplace transforms to solve ordinary and partial differential equations. It also includes Fourier series, Fourier Integral representation and Fourier transform of special functions, properties, operations and using them in partial differential equations. (prereq: MA 232 or MA 2323 , and MA 235 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Find Laplace and inverse Laplace transforms using a table
 Find Fourier transforms
 Solve linear differential equations and systems of equations with input functions, such as: continuous, piecewise continuous, unit step, impulse and periodic
 Solve certain types integral, and integrodifferential equations
 Solve certain classes of linear partial differential equations
Prerequisites by Topic
 Improper integrals
 Infinite series
 Linear differential equations
Course Topics
 Basic properties of Laplace transforms and transforms of special functions
 Transforms of derivatives and integrals and derivatives of transform
 Application to differential equations
 The unit step function
 The Dirac delta function
 Applications of step and impulse functions
 Periodic functions and their applications
 Convolution and applications
 Solving integral equations
 Fourier series
 Fourier integral representation
 Fourier transforms and its properties
 Fourier sine and cosine transforms
 Application of Fourier transforms to partial differential equation
Coordinator Dr. Yvonne Yaz



MA 383  Linear Algebra 3 lecture hours 0 lab hours 3 credits Course Description Topics include the use of elementary row operations to solve systems of linear equations, linear dependence, linear transformations, matrix operations, inverse of a matrix, determinants, subspaces, null spaces, column spaces, dimension and rank, eigenvalues and eigenvectors, diagonalization of matrices, and similarity. (prereq: MA 231 or MA 2314 or MA 3501 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Learn the basic theory of linear algebra
 Apply the basic row operations to solve systems of linear equations
 Solve a matrix equation and a vector equation
 Understand the concept of linear dependence and independence
 Understand matrix transformations and linear transformations and the relationship between them
 Perform all matrix operations, be able to find the inverses and determinants of matrices
 Understand the concept of a subspace and basis
 Describe the column and null spaces of a matrix and find their basis and dimensions, and the rank of a matrix
 Understand the concept of similarity
 Find the eigenvalues and eigenvectors of a matrix
Prerequisites by Topic
 Differential and integral calculus
 Basic vector mathematics
Course Topics
 Introduction to systems of linear equation and solving them using matrices, row operations
 Vectors, vector and matrix equations
 Matrix operations
 Vector spaces including bases, dimension, rank and nullity
 Linear independence
 Matrix transformations, linear transformations and their relations
 Similarity
 Eigenvalues, eigenvectors and their applications
 Diagonalization
 Applications
Coordinator Dr. Yvonne Yaz



MA 384  Statistical Methods for Use in Research 3 lecture hours 0 lab hours 3 credits Course Description This course is an introduction to the techniques and methods used in research and seen in published research papers. It assumes a knowledge of the statistical methods generally encountered in an introductory, calculusbased statistics course. Methods such as multiple and nonlinear regression, sequential models regression, twoway analysis of variance, contingency tables, and nonparametric statistical methods from the basis of this course. (prereq: MA 262 or MA 3611 or MA 2410 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand the underlying assumptions for the use of any statistical test and understand why those assumptions exist
 Perform single and multiplevariable regression analyses and be able to provide the correct interpretation of applied hypothesis tests
 Perform and interpret the meaning of a lackoffit analysis
 Perform and interpret analyses of categorical data
 Perform and interpret the application of various normality tests
 Perform and interpret stepwise regression techniques
 Correctly assess nonparametric situations, including knowing which nonparametric statistic to apply, which nonparametric hypothesis test to apply, and how to interpret the results obtained using such statistics and performing such hypothesis tests
 Correctly determine a statistical test’s power
 Correctly determine the sample size necessary for a given statistical situation
Prerequisites by Topic
 Differentiation and partial differentiation
 Integration and multiple integration
 Basic inferential statistical knowledge
 Knowledge of hypothesis testing
Course Topics
 Simple linear regression and correlation
 Multiple and nonlinear regression, including sequential models
 Contingency tables
 Tests of normality
 Twoway analysis of variance
 Nonparametric statistics
 Power and sample size
Coordinator Dr. Won Chul Song



MA 385  Modern Algebra with Applications 3 lecture hours 0 lab hours 3 credits Course Description This course is an introduction to abstract algebra with a focus on elementary group theory and some of its applications. Topics include modular arithmetic, groups, subgroups, isomorphism, external direct products, rings, integral domains and fields. Applications include error checking/correction and the RSA encryption algorithm. (prereq: MA 383 or MA 2830 or MA 2310 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Perform modular arithmetic operations including powers and inverses of large numbers
 Identify whether or not a set together with a binary operation is a group
 Relate divisibility facts to properties of cyclic groups
 Identify isomorphic groups
 Perform arithmetic operations with external direct products of cyclic groups
 Prove basic theorems involving groups
 Perform errorchecking and errorcorrection computations including the ISBN system
 Use the RSA algorithm to encrypt and decrypt large numbers
 Solve seconddegree equations in various rings
 Prove basic theorems involving rings
Prerequisites by Topic Course Topics
 Division algorithm, Euclidean algorithm, modular arithmetic and errorchecking
 Binary operations and groups
 Finite groups and subgroups
 Cyclic groups
 Mappings and isomorphisms
 External direct products
 RSA encryption and modular arithmetic with large numbers
 Fundamental Theorem of Finite Abelian Groups
 Rings
 Impossible constructions
 Reviews
 Exams
Coordinator Dr. Anthony van Groningen



MA 386  Functions of a Real Variable 3 lecture hours 0 lab hours 3 credits Course Description This course looks at the foundations of calculus with more rigor, using the concepts of sequences and limits to understand continuity, differentiation and integration in greater depth than is possible in the calculus sequence. (prereq: MA 232 or MA 2323 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand the basic topology of the real number line
 Understand the basic definitions and theorems concerning limits of sequences
 Determine the convergence of sequences
 Understand the concept and know the basic properties of continuous functions
 Understand the concept and know the basic properties of differentiable functions
 Understand the Riemann integral and the Fundamental Theorem of Calculus
Prerequisites by Topic Course Topics
 Mathematical induction
 Real number line
 Sequences
 Limits
 Continuity
 Differentiability
 Integrability
Coordinator Dr. Chunping Xie



MA 387  Partial Differential Equations 3 lecture hours 0 lab hours 3 credits Course Description This course provides a smooth transition from a course in elementary ordinary differential equations to more advanced topics in a first course in partial differential equations, with heavier emphasis on Fourier series and boundary value problems. Topics covered includes separation of variables, classification of second order equations and canonical form, Fourier series, the onedimensional and twodimensional wave equation and heat equation, and Laplace’s equation. It also covers some applications, such as vibrating string, vibrating membrane, vibration of beams, heat conduction in bars and rectangular regions, etc. (prereq: MA 235 , MA 232 or MA 2323 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Write Fourier series of functions with period 2p
 Write Fourier series of functions with arbitrary periods
 Be able to write Fourier series of nonperiodic functions using halfrange expansions
 Write the complex form of Fourier series
 Solve onedimensional wave equation using method of separation of variables and apply it to vibrating strings
 Solve onedimensional heat equation using method of separation of variables and apply it to heat conduction in bars
 Solve twodimensional wave and heat equations using method of separation of variables
 Solve twodimensional Laplace’s equation in rectangular coordinates
 Solve twodimensional wave equation in polar coordinates and apply it to vibrating membranes
 Solve twodimensional Laplace’s equation in polar coordinates and use it in applications.
Prerequisites by Topic
 Infinite series
 Ordinary differential equations
Course Topics
 What is a partial differential equation and interpreting a given partial differential equation
 Periodic functions
 Fourier series
 Fourier series of functions with arbitrary periods
 Halfrange expansions: Fourier sine and cosine series
 Complex form of Fourier series
 Forced oscillations
 Modeling: Vibrating string and onedimensional wave equation
 Solution of onedimensional wave equation using method of separation of variables
 D’Lambert’s method of solving onedimensional wave equation
 Solution of onedimensional heat equation using method of separation of variables
 Heat conduction in bars: Varying the boundary conditions
 The twodimensional wave and twodimensional heat equations
 Laplace’s equation in rectangular coordinates
 The Poisson’s Equation: The method of eigenfunction expansion
 Neumann and Robin conditions
 Laplacian in various coordinate systems
 Twodimensional wave equation in polar coordinates: Vibration of a circular membrane
 Twodimensional Laplace’s equation in polar coordinates
Coordinator Dr. Yvonne Yaz



MA 388  Introduction to Number Theory 3 lecture hours 0 lab hours 3 credits Course Description Number theory is primarily concerned with the properties of the integers. While the subject has long been thought of as quintessentially “pure” mathematics, recent developments in fields such as cryptography have renewed interest in it. Topics include mathematical induction; divisibility and primes; the Euclidean algorithm; linear Diophantine equations; modular arithmetic; primality testing; and continued fractions. (prereq: MA 231 or MA 2314 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Write elementary proofs
 Use the principle of mathematical induction
 Apply the Euclidean algorithm and solve linear Diophantine equations
 Perform modular arithmetic
 Apply Fermat’s Little Theorem and Euler’s Theorem
 Understand the distribution of the prime numbers
 Test for primality of integers
 Find continued fraction expressions for real numbers (optional)
 Understand the RSA encryption algorithm
 Use Quadratic Reciprocity to compute Legendre symbols
Prerequisites by Topic Course Topics
 Introduction to number theory, mathematical proof, and induction
 Euclidean algorithm, divisibility, the GCD, and linear Diophantine equations
 Fundamental theorem of arithmetic
 Congruences and Fermat’s little theorem
 The phi function and Euler’s theorem
 Chinese remainder theorem
 Distribution of primes; primality testing
 Successive squaring, kth roots, and RSA
 Primitive roots and discrete logarithms
 Quadratic reciprocity
Coordinator Dr. Anthony van Groningen



MA 390  Financial Mathematics 4 lecture hours 0 lab hours 4 credits Course Description This course will the last course which prepares students for the second actuarial exam, referred to as Exam FM by the SOA, and Exam 2 by the CAS. It will review and/or cover the topics such as time value of money, annuities, loans, bonds, cash flow, and portfolios, immunization, general derivatives, options, hedging, forwards and futures and swaps. (prereq: BA 2503 , AS student, or consent of the program director) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Demonstrate knowledge of the fundamental concepts of financial mathematics
 Demonstrate an ability to apply these concepts in calculating present and accumulated values for various streams of cash flows as a basis for use in: reserving, valuation, pricing, asset/liability management, investment income, capital budgeting, and valuing contingent cash flows
 Show introductory knowledge of financial instruments, including derivatives, and the concept of noarbitrage as it relates to financial mathematics
 Successfully complete the FM exam
Prerequisites by Topic
 Business finance and accounting knowledge
Course Topics
 General cash flows and portfolios
 Immunization
 General derivatives
 Options
 Hedging and investment strategies
 Forwards and futures
 Swaps
Coordinator Dr. Yvonne Yaz



MA 461  Applied Probability Models 4 lecture hours 0 lab hours 4 credits Course Description This is an advanced probability course which covers topics such as Poisson processes, Markov chains, Markov decision process, inventory theory, queueing theory and reliability theory. (prereq: MA 2630 and MA 2631 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
Prerequisites by Topic
 Fundamentals of probability
Coordinator Dr. Yvonne Yaz



MA 499  Independent Study 3 lecture hours 0 lab hours 3 credits Course Description A student enrolled in this course is afforded the opportunity to pursue a specialized topic in his or her chosen field of study. After an approved area of study has been selected, weekly meetings with the course advisor are required. A final report, the format of which is left to the discretion of the advisor, is required at the end of the term. (prereq: none) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
Prerequisites by Topic Course Topics Coordinator Dr. Kseniya Fuhrman



MA 1204  Quantitative Reasoning for Health Care Professionals 4 lecture hours 0 lab hours 4 credits Course Description This course addresses mathematical concepts and calculations frequently encountered by health care professionals. Topics include fundamental operations; ratio, proportion and percentage; magnitude and scale; manipulation and conversion of units; rounding and scientific notation. Additional topics include solving equations involving ratio, proportion and rate; interpretation of graphs; evaluation of formulas, including formulas encountered in statistics. (prereq: only open to Nursing students) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Perform arithmetic operations involving ratio, proportion, percentage and rate
 Use rounding principles and scientific notation to report results of calculations
 Interpret numerical values as magnitude, quantity and scale
 Interpret and convert units
 Solve and interpret equations involving ratio, proportion, percentage and rate
 Solve and interpret applied problems relating to concentrations and mixtures
 Interpret graphs involving time relationships
 Evaluate formulas encountered in pediatric medication and statistics
Prerequisites by Topic Course Topics
 Fundamental operations involving fractions, decimals and percents
 Solving equations involving ratio, percentage and rate
 Conversion of units between systems of measurement of magnitude, quantity and scale including temperature, angles and volume
 Health care applications involving ratio, proportion, rate and percentage including dosage calculations, angle measurement and IV rates
 Read and interpret time series graphs such as EKG
 Evaluate formulas commonly encountered in statistics
Coordinator Edward Griggs



MA 1830  Transition to Advanced Topics in Mathematics 4 lecture hours 0 lab hours 4 credits Course Description Introduction to proof techniques to be used in upperlevel mathematics courses. Topics include logic and proofs, set theory, relations and partitions, functions, and cardinality of sets. (prereq: only open to AS students) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Demonstrate proficiency in elementary logic, including using truth tables to prove logical equivalence
 Manipulate logical sentences symbolically and semantically: for example, apply DeMorgan’s Law to construct denials
 Demonstrate familiarity with the natural numbers, integers, rational numbers, real numbers, and complex numbers
 Demonstrate proficiency in interpreting and manipulating existential and universal quantifiers
 Read and construct proofs using direct and indirect methods
 Choose methods of proof appropriately
 Read and construct proofs involving quantifiers
 Demonstrate proficiency in elementary set theory including construction of sets, subsets, power sets, complements, unions, intersections, and Cartesian products
 Interpret unions and intersections of indexed families of sets
 Read and construct proofs involving set theoretic concepts
 Apply the principle of mathematical induction and its equivalent forms
 Manipulate summations in sigma notation
 Read and construct proofs related to relations, equivalence relations, and partitions of sets
 Demonstrate familiarity with functions as relations; injections, surjections, and bijections
 Construct functions from other functions: for example, compositions, restrictions, and extensions
 Read and construct proofs related to functions
 Demonstrate familiarity with cardinality for finite, countable, and uncountable sets
Prerequisites by Topic Course Topics
 Elementary logic with truth tables
 Quantifiers
 Methods of proof
 Elementary set theory
 Operations with sets including indexed families of sets
 Principle of mathematical induction and its equivalent forms
 Cartesian products
 Relations, equivalence relations, and partitions of sets
 Functions, surjections, and injections
 Cardinality of sets
Coordinator Dr. Anthony van Groningen



MA 1840  Computer Applications in Applied Mathematics 4 lecture hours 0 lab hours 4 credits Course Description This course introduces students to computer applications used for solving mathematical problems. Emphasis is placed on learning advanced functions in Microsoft Excel and MATLAB. Topics include problem formulation, model development and implementation, data analysis, and the use of computer graphics in solutions of various problems. The course material is presented as a combination of lecture and handson exercises. (prereq: none) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Apply a variety of computer tools to solve a wide range mathematical problems
 Analyze and present data
 Use computer tools to create professional presentations of solutions
 Work with advanced formulas and functions in Microsoft Excel
 Write macros in Microsoft Excel
 Program scripts and functions using the MATLAB development environment
 Implement selection and loop statements
 Generate plots for use in reports and presentations
 Fit curves to data
Prerequisites by Topic Course Topics
 Working with data and Excel tables
 Performing calculations on data
 Formatting
 Filters
 Formulas and functions
 Charts and graphics
 PivotTables and PivotCharts
 Macros and forms
 MATLAB environment
 MATLAB scripts
 Selection statements
 Loop statements
 Plotting techniques
 Fitting curves to data
Coordinator Dr. Kseniya Fuhrman



MA 2310  Discrete Mathematics I 3 lecture hours 0 lab hours 3 credits Course Description This course provides an introduction to discrete mathematics as it applies to computer science. Topics include sets, logic, relations, functions, recursion, Boolean algebra, and graph theory. A student cannot receive credit for both MA 2310 and MA 1830 . (prereq: sophomore standing) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Illustrate by examples the basic terminology of functions, relations, and sets
 Illustrate by examples, both discrete and continuous, the operations associated with sets, functions, and relations
 Apply functions and relations to problems in computer science
 Manipulate formal methods of symbolic propositional and predicate logic
 Demonstrate knowledge of formal logic proofs and logical reasoning through solving problems
 Illustrate by example the basic terminology of graph theory
 Apply logic to determine the validity of a formal argument
 Identify a relation; specifically, a partial order, equivalence relation, or total order
 Identify a function; specifically, surjective, injective, and bijective functions
 Illustrate by examples tracing Euler and Hamiltonian paths
 Construct minimum spanning trees and adjacency matrices for graphs
Prerequisites by Topic
 Basic concepts of college algebra
 Basic concepts of set theory
Coordinator Dr. Chunping Xie



MA 2314  Calculus III 4 lecture hours 0 lab hours 4 credits Course Description This course is a continuation of MA 137. Topics include L’Hȏpital’s rule, improper integrals, parametric equations, polar coordinates, vector algebra, infinite series with tests for convergence, power series, Taylor and Maclaurin series, and operations with series. (prereq: MA 137 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Use L’Hȏpital’s rule to evaluate a limit
 Evaluate improper integrals
 Find the length of the arc of a curve
 Eliminate the parameter from parametric equations
 Draw graphs of parametric equations and determine the direction of travel for an increasing parameter
 Find first and second derivatives of parametric functions
 Find the arc length for parametric curves
 Convert between rectangular and polar coordinates
 Draw graphs of polar curves
 Find area and arc length in polar coordinates
 Perform operations using vector algebra
 Find dot products, cross products, and equations of lines and planes in three dimensions
 Test sequences for convergence and divergence
 Test infinite series for convergence and divergence
 Find the interval of convergence for a power series
 Perform algebraic and calculus operations on power series
 Use Taylor and Maclaurin series to approximate functions
Prerequisites by Topic
 The basic principles of algebra
 The basic principles of trigonometry
 Differentiation and integration of algebraic and transcendental functions
 Limits
 Understanding of the definition of the definite integral
Course Topics
 L’Hȏpital’s rule
 Improper integrals
 Arc length
 Parametric equations
 Polar coordinates and graphs
 Vectors, lines, and planes
 Sequences
 Infinite series and tests for convergence
 Power series and intervals of convergence
 Taylor and Maclaurin series
Coordinator Dr. Chunping Xie



MA 2320  Introduction to Graph Theory 3 lecture hours 0 lab hours 3 credits Course Description This course introduces a sampling of fundamental concepts and results in graph theory. Topics include graph isomorphisms, trees and connectivity, matching and covering, planarity and colouring, and Ramsey’s Theorem. Graph algorithms for solving the assignment problem and the maxflow problem will also be discussed. (prereq: MA 1830 or MA 2310 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Demonstrate knowledge of basic terminology associated with graphs, such as isomorphisms, trees, connectivity, planarity, colouring, and matchings
 Demonstrate knowledge of fundamental results in graph theory, such as Konig’s Theorem, Hall’s Theorem, Kuratowski’s Theorem and the 4Colour Theorem
 Be able to apply various techniques (e.g. mathematical induction, proof by contradiction) to construct basic proofs for statements involving graphs
 Model simple real world problems using graph theory
 Be able to solve instances of the assignment problem and the maxflow problem using appropriate graph algorithms
Prerequisites by Topic
 Basic concepts of college algebra
 Basic concepts of set theory
 Basic concepts of logic and proofs
Course Topics
 Basic definitions and notions for graphs
 Matching and covering
 Planarity and colouring
 Graph algorithms
 Ramsey Theory
Coordinator Edward Griggs



MA 2323  Calculus IV 3 lecture hours 0 lab hours 3 credits Course Description This course is a continuation of MA 2314 and an introduction to multivariate calculus. Topics include surfaces in three dimensions, partial derivatives, double and triple integrals with applications to areas, volumes, and moments. (prereq: MA 2314 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Sketch surfaces in three dimensions
 Find first and second partial derivatives
 Find the total differential of a function of more than one variable and use it to approximate error
 Use chain rules to find derivatives and partial derivatives
 Find implicit partial derivatives
 Determine the maximum, minimum, and saddle points on a surface
 Set up and evaluate double integrals using rectangular and polar coordinates
 Find areas, volumes, and moments using double integrals
 Set up and evaluate triple integrals
 Use triple integrals to find volumes and moments of solids
 Use integrals in cylindrical or spherical coordinates to find volumes and moments
Prerequisites by Topic
 The basic principles of algebra
 The basic principles of trigonometry
 Differentiation and integration of algebraic and transcendental functions
 Applications of integration
 Integration techniques
 L’Hȏpital’s rule
 Polar coordinates
Course Topics
 Surfaces in three dimensions
 Functions of several variables
 Partial derivatives
 Extrema of functions of two variables
 Double integrals, area, volume, and moments
 Triple integrals, volume, moments, cylindrical and spherical coordinates
Coordinator Edward Griggs



MA 2410  Statistics for AS 4 lecture hours 0 lab hours 4 credits Course Description The course is designed to expose actuarial science majors to the statistical tools needed to make decisions based on the computed probability of occurrence. Both descriptive and inferential statistics will be considered. This course is designed to satisfy all the required topics of SOA (Society of Actuaries) VEE Mathematical Statistics curriculum. (prereq: MA 2323 , AS student, or consent from AS program director) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Choose which probability distribution applies to a given statistical situation
 Perform a complete hypothesis test
 Correctly calculate and interpret a pvalue
 Recognize the similarities between the various hypothesis tests and the formulas used by these tests
 Construct estimators using method of moments and maximum likelihood estimator
 Demonstrate understanding of sampling distributions
 Perform analysis of variance when appropriate and interpret the results
 Describe properties of estimators, including mean squared errors and UMVUE
 Apply NeymanPearson Lemma and likelihood ratio tests
 Construct confidence intervals for mean, the difference of two means, the difference of proportions and variance
 Apply analysis of variance and chisquare goodnessoffit tests
Prerequisites by Topic
 Differential and integral calculus (both single and multivariable)
Course Topics
 Major probability distributions used in hypothesis testing including normal, studentt, chisquare, and F
 Sampling distribution and central limit theorem
 Constructing estimators using such as the method of moments and maximum likelihood estimator
 Properties of estimators, including mean squared errors and UMVUE
 Theory of hypothesis test, including NeymanPearson Lemma and likelihood ratio test
 Onesample and twosample hypothesis testing
 Confidence intervals for mean, the difference of two means, the difference of proportions and variance
 Analysis of variance and chisquare goodnessoffit test
Coordinator Dr. Won Chul Song



MA 2411  Time Series Analysis 4 lecture hours 0 lab hours 4 credits Course Description This course is designed for actuarial science majors taking the sequence of actuarial exams. A time series is a collection of measurements taken at different points in time. This course will introduce the theory and practice of analyzing time series, emphasizing practical skills. In particular, these skills will include providing compact descriptions of time series data, interpretation of time series data, forecasting future values based on known time series data, hypothesis testing with respect to time series analysis, and simulation using time series models. (prereq: MA 232 or MA 2323 , MA 2630 , MA 2410 or consent of the AS program director) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 State the basic theory of timeseries analysis and forecasting approaches
 Synthesize the relevant statistical knowledge and techniques for forecasting
 Identify, define, and formulate forecasting problems and use statistical software for the analysis of time series and forecasting
 Interpret analysis results and make recommendations for the choice of forecasting methods
 Produce and evaluate forecasts for a given time series
 Present analysis results of forecasting problems
 Be able to read published articles concerning time series
Prerequisites by Topic
 Calculus (single variable and multivariable)
 Probability theory and application
 Statistics including regression
Course Topics
 Simple linear regression
 Multiple linear regression
 Model building
 Residual analysis
 Time series regression
 Exponential smoothing
Coordinator Dr. Won Chul Song



MA 2630  Probability I for AS 4 lecture hours 0 lab hours 4 credits Course Description This course introduces elementary probability theory, which includes basic probability concepts such as counting, sets, axioms of probability, conditional probability and independence, Bayes’ theorem, discrete random variables, common discrete distributions, joint distributions, properties of expectation, moment generating functions, and limit theorems. (prereq: sophomore standing in AS program or consent of instructor) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Perform basic set theory operations including union, intersection, and apply them to probability situations
 Understand the differences between mutually exclusive events and independent events and apply this knowledge to probability situations
 Understand the differences and similarities of combinations and permutations and how combinations are used to evaluate probabilities
 Understand the concept of conditional probability and how it extends to the Law of Total Probability and Bayes’ Rule
 Use various discrete probability distributions to determine probabilities
 Use, understand, derive and use discrete probability mass functions, distribution functions, and momentgenerating functions
 Understand, derive, and use discrete joint probability functions
 Understand the meaning and relevance of variance and standard deviation and how it relates to probability calculations
 Understand and use the results of the Central Limit Theorem
 Use a transformation function to transform one probability mass function into another
Prerequisites by Topic Course Topics
 Union and intersection notation, theory, and examples
 Mutually exclusive events and independent events
 Addition and multiplication rules for probability
 Combinatorics
 Conditional Probability
 Law of Total Probability
 Bayes’ Rule
 Discrete probability distributions such as the binomial, Poisson, negative binomial, uniform, geometric, hypergeometric, etc.
 Discrete probability mass functions
 Discrete cumulative distribution functions
 Discrete momentgenerating functions
 Continuous probability distributions such as the Gaussian (normal) distribution, Studentt, chisquared, F, exponential, gamma, beta, etc.
 Continuous probability density functions
 Continuous cumulative density functions
 Continuous momentgenerating functions
 Measures of dispersion (including variance)
 Transformations of random variables
Coordinator Dr. Yvonne Yaz



MA 2631  Probability II for AS 4 lecture hours 0 lab hours 4 credits Course Description This course continues where MA 2630 ended. In particular, topics of discussion will include continuous probability distributions such as the uniform, normal, exponential, gamma, beta, Cauchy, and Weibull distributions, both discrete and continuous joint probability distributions, and additional expectation results, such as momentgenerating functions, that were not discussed in MA 2630 . (prereq: MA 2630 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand and apply continuous probability distributions to appropriate probability situations
 Understand, derive, and use continuous probability density functions, conditional probability density functions, marginal functions, and momentgenerating functions
 Understand, derive, and use continuous joint probability functions
 Understand the meaning and relevance of and use measures of dispersion for continuous multivariable probability distributions
 Understand, calculate, and use covariance
 Understand, calculate, and apply to correlation coefficient appropriate situations
 Perform transformations of continuous random variables
 Form and use linear combination of random variables with respect to calculation of probabilities and moments
Prerequisites by Topic
 Multivariable calculus
 Discrete random variables
Course Topics
 Continuous probability distributions such as the Gaussian (normal) distribution, Studentt, chisquared, F, exponential, gamma, beta, etc.
 Continuous probability density functions
 Continuous cumulative density functions
 Continuous momentgenerating functions
 Continuous joint probability functions, joint probability density functions, and joint cumulative density functions
 Conditional and marginal distributions and densities
 Moments for the discrete and continuous joint functions considered
 Joint momentgenerating functions
 Measures of dispersion for multivariable probability distributions
 Covariance
 Correlation coefficients
 Transformations of continuous random variables
 Linear combinations of random variables including probabilities and moments
Coordinator Dr. Yvonne Yaz



MA 2830  Linear Algebra for Math Majors 4 lecture hours 0 lab hours 4 credits Course Description Topics include the use of elementary row operations to solve systems of linear equations, linear independence, matrix operations, inverse of a matrix, linear transformations, vector spaces and subspaces, coordinate systems and change of bases, determinants of matrices and their properties, eigenvalues, eigenvectors, diagonalization, inner product and orthogonality, the GramSchmidt Process, and the leastsquares problem. Particular emphasis is given to proper mathematical reasoning and presentation of solutions. The students will use MATLAB to explore certain applications. (prereq: MA 1830 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand the basic theory of linear algebra
 Apply the basic row operations to solve systems of linear equations
 Solve a matrix equation and a vector equation
 Understand the concept of linear dependence and independence
 Understand matrix transformations and linear transformations and the relationship between them
 Perform all matrix operations, be able to find the inverses of matrices
 Understand concepts of vector space, subspace and basis and be able to change bases
 Describe the column and null spaces of a matrix and find their dimensions
 Find the rank of a matrix
 Be able to find the eigenvalues and corresponding eigenvectors of matrices
 Be able to identify a diagonalizable matrix and diagonalize it
 Understand the relationship between eigenvalues and linear transformations
 Understand the concepts of orthogonality and orthogonal projections
 Apply the GramSchmidt Process to produce orthogonal bases
 Find the leastsquares solution to a system of linear equations
Coordinator Dr. Kseniya Fuhrman



MA 3320  Discrete Mathematics II 3 lecture hours 0 lab hours 3 credits Course Description This course continues the introduction of discrete mathematics begun in MA 2310. Emphasis is placed on concepts applied within the field of computer science. Topics include logic and proofs, number theory, counting, computational complexity, computability, and discrete probability. A student cannot receive credit for both MA 3320 and MA 1830. (prereq: MA 2310 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Apply conditional probability to identify independent events
 Apply Bayes’ rule and demonstrate an understanding of its implications
 Be familiar with standard complexity classes
 Derive closedform and asymptotic expressions from series and recurrences for growth rates of processes
 Identify a given set as countable or uncountable
 Calculate the number of possible outcomes of elementary combinatorial processes such as permutations and combinations
 Illustrate by examples the properties of primes
 Apply the Chinese remainder theorem
 Synthesize induction hypotheses and simple induction proofs
 Illustrate by examples proof by contradiction
Prerequisites by Topic
 Predicate logic
 Recurrence relations
 Fundamental structures
Coordinator Dr. Chunping Xie



MA 3501  Engineering Mathematics I 4 lecture hours 0 lab hours 4 credits Course Description This course provides topics to bridge the technical calculus sequence to the university’s calculus sequence. Topics include vector algebra; review of single variable calculus, including differentiation of elementary functions, the mean value theorem, antiderivatives, and definite integrals; selected methods of integration including partial fractions decomposition; functions of several variables including partial differentiation and multiple integration in cylindrical and spherical coordinates. (prereq: one year of technical calculus or equivalent) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Differentiate and integrate elementary functions
 Evaluate improper integrals on an infinite interval
 Integration by partial fractions decomposition
 Evaluate partial derivatives of functions of multiple variables
 Use the chain rule to find derivatives of functions of multiple variables
 Evaluate the total differential to estimate change in a function of multiple variables
 Evaluate double integrals in rectangular and polar coordinates
 Evaluate triple integrals in rectangular, cylindrical, and spherical
 Perform operations using vector algebra
 Evaluate dot and cross products of vectors
 Find equations of planes in three dimensions
 Parameterize lines in three dimensions
 Solve geometric problems involving lines and planes
Prerequisites by Topic
 Differentiation of trigonometric, inverse trigonometric, exponential and logarithmic functions.
 Basic integration including substitution.
Course Topics
 Review of differentiation of elementary functions
 The mean value theorem
 Review of antiderivatives and basic integration techniques, definite integrals, and the fundamental theorem of calculus
 Improper integrals on an infinite interval
 Integration by partial fractions decomposition
 Functions of two or more variables
 Partial differentiation, the chain rule, and the total differential
 Double integrals in rectangular and polar coordinates
 Triple integrals in rectangular, cylindrical, and spherical
 Threedimensional coordinate systems
 Vector algebra including dot and cross products of vectors
 Lines and planes in threedimensional space
Coordinator Dr. Anthony van Groningen



MA 3502  Engineering Mathematics II 4 lecture hours 0 lab hours 4 credits Course Description Solution of first order equations, higher order linear equations and initial value problems, the methods of undetermined coefficients, variation of parameters, and Laplace transforms. (prereq: MA 225, MA 231 /MA 2314 or equivalent) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Determine the solution of a first order differential equations by the method of separation of variables
 Solve exact equations
 Determine appropriate integrating factors for first order linear equations
 Determine the general solution of higher order linear homogeneous equations with constant coefficients
 Determine the general and particular solutions of certain linear nonhomogenous equations using the methods of undetermined coefficients and variation of parameters
 Determine the Laplace transform and inverse Laplace transform of certain elementary functions
 Solve certain linear differential equations using Laplace transforms
Prerequisites by Topic
 Differentiation of elementary functions for all topics
 Integration techniques for solving differential separable and exact equations and for variation of parameters
 Improper integrals for Laplace transforms
Course Topics
 Basic concepts of differential equations
 Solution of first order equations by separation of variables
 Solution of exact equations
 Solution of first order linear nonhomogeneous equations
 Solution of higher order linear homogeneous differential equations with constant coefficients
 Solution of higher order linear nonhomogeneous differential equations using the method of undetermined coefficients
 Solution of higher order linear nonhomogeneous differential equations using the method of variation of parameters
 Introduction to Laplace transforms
 Laplace transforms of elementary functions
 Inverse Laplace transforms
 Operational properties: Laplace transforms and inverse Laplace transforms involving transforms of derivatives, derivatives of transforms, exponential shift (translation on the saxis) and Heaviside function (translation on the taxis), Dirac delta function and periodic functions
 Solution of linear differential equations using Laplace transforms
Coordinator Dr. Anthony van Groningen



MA 3611  Biostatistics 3 lecture hours 0 lab hours 3 credits Course Description This course provides an introduction to biostatistics for biomedical engineering students. As a result of this course the students are expected to understand and prepare statistical analyses of data from physiological systems in the laboratory and clinical environment. Students learn basic probability theory that includes discrete and continuous probability distributions. They learn how to apply that theory to hypothesis testing and understand the difference between a ztest and ttest, one and twosample inference hypothesis testing, and Analysis of Variance. Additional concepts covered include hypothesis formulation and testing, both parametric and nonparametric. Either the statistical package SAS or the statistical package SPSS will be introduced to the students and will be used to perform statistical analyses. Finally, journal articles from the New England Journal of Medicine (NEJM) containing significant statistical components will be considered in class. (prereq: MA 136 ) (coreq: MA 137 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Recognize and evaluate conditional probability situations such as Bayes’ Rule, specificity, sensitivity, predictive value positive, and predictive value negative
 Set up and evaluate inferences using hypothesis tests and confidence intervals
 Perform hypothesis tests for one and twosample situations
 Recognize when analysis of variance (ANOVA) is applicable, and subsequently be able to apply and evaluate ANOVA calculations
 Recognize when nonparametric situations are present and then be able to apply the correct nonparametric test, evaluate it, and interpret it
 Use SAS (or SPSS if it is the statistical package being used) when appropriate
 Read and interpret the statistical content of assigned articles in the NEJM
Prerequisites by Topic Coordinator Dr. Won Chul Song



MA 3710  Mathematical Biology 3 lecture hours 0 lab hours 3 credits Course Description This course is an overview of several techniques used in the development and analysis of mathematical models that illustrate various biological processes. The topics covered involve applications of ordinary and partial differential equations, dynamical systems, and statistical analysis. Applications include population models, infectious disease and epidemic models, genetics, tumor growth, and DNA sequencing. (prereq: MA 235 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Interpret biological assumptions in terms of mathematical equations
 Construct mathematical models to illustrate a biological processes
 Write computer simulations for a biological model
 Analyze a model numerically and graphically
 Find equilibria of system of equations
 Perform local stability analysis
 Solve counting problems involving the addition and multiplication rules, permutations, and combinations
 Calculate discrete probability
Prerequisites by Topic
 Know the techniques of limits, differentiation, and integration
 Be able to determine the solution of firstorder differential equations by the method of separation of variables
 Be able to determine appropriate integrating factors for firstorder linear differential equations
Course Topics
 Introduction to mathematical biology
 Constructing a model
 Exponential and logistic growth
 Populationgenetic models
 Models of interaction among species
 Epidemiological models of disease spread
 MATLAB review
 Numerical and graphical techniques
 Finding equilibrium
 Performing local stability analysis: one variable model
 Finding an approximate equilibrium
 Matrices, Eigenvalues, Eigenvectors
 Performing local stability analysis: nonlinear models with multiple variables
 Counting principles: addition and multiplication rules
 Permutations
 Combinations
 Arrangements with repetitions
 Probability
 Conditional probability and independence of events
Coordinator Dr. Kseniya Fuhrman



MA 4980  Topics in Mathematics 04 lecture hours 0 lab hours 04 credits Course Description This course gives an opportunity to the students to take a course in a topic that is not covered in other mathematics courses offered. Topics covered can be determined by the faculty and the students based on their mutual interest. (prereq: instructor consent) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
Prerequisites by Topic
 Depend on the topic of choice.
Coordinator Dr. Yvonne Yaz

Mechanical Engineering 


ME 190  Computer Applications in Engineering I 2 lecture hours 2 lab hours 3 credits Course Description The purpose of this course is to familiarize students with the modern computer tools required for engineering practice and teach them how to apply these tools to solve practical engineering problems. Topics include problem formulation, model development, algorithm development, and the use of numerical methods and computer graphics in the solution of engineering problems. Laboratory exercises will involve the use of various numerical and graphical software packages. (prereq: MA 127 or equivalent) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Apply problemsolving skills to engineering problems
 Present formal solutions to engineering problems
 Apply modern computational tools to solve mechanical and industrial engineering problems
Prerequisites by Topic
 College trigonometry and algebra
Course Topics
 Problem solving methodologies; introduction to algorithm development
 Working with arrays, plotting
 Programming  loops, logic, and functions
 Flowcharts
 Curve fitting and the leastsquares method
 Solving system of equations
 Matrix methods
 Importing and exporting data
 Numerical integration and differentiation
 Optimization
Laboratory Topics
 Problem solving
 Plotting data
 Roots of equations
 Numerical integration
 Solution of simultaneous equations
 Optimization
Coordinator Dr. Michael Cook



ME 205  Engineering Statics 4 lecture hours 0 lab hours 4 credits Course Description This is a study of force systems acting on bodies that are not in motion. The course includes analysis of forces in trusses, frames and machine components; additional topics include friction, location of centroids, and evaluation of area and mass moments of inertia. Not for credit for students who have credit in AE 200 . (prereq: MA 137 , high school physics) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Draw free body diagrams for static systems
 Perform 2D equilibrium analysis using scalar analysis
 Perform 3D equilibrium analysis using vector analysis
 Determine internal forces in trusses, frames and machines
 Analyze the effect of friction in static systems
 Compute area and mass moments of inertia of shapes and bodies
Prerequisites by Topic
 Vector mathematics
 Physics of mechanics
 Integral calculus
Course Topics
 Introduction to mechanics (unit systems, forces, vector mathematics) (2 classes)
 2D and 3D particle equilibrium (4 classes)
 Moments, force/couple systems (5 classes)
 2D and 3D rigid body equilibrium (7 classes)
 Analysis of trusses, frames, and machines (5 classes)
 Friction (3 classes)
 First area moments, centroids (by composite shapes and direct integration) (3 classes)
 Area moment of inertia (by composite shapes and direct integration) (3 classes)
 Mass moment of inertia (by composite shapes and direct integration) (3 classes)
 Testing and review (5 classes)
Coordinator Dr. Joseph Musto



ME 206  Engineering Dynamics 4 lecture hours 0 lab hours 4 credits Course Description This is the study of motion and the forces which affect the motion. This course includes the study of rectilinear motion, curvilinear motion, plane motion, dynamic force analysis, work and energy, and impulse and momentum. (prereq: ME 205 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Determine the position, velocity, and acceleration of particles subjected to rectilinear translation
 Determine the trajectory of projectiles given initial conditions
 Determine the position, velocity and acceleration of given points of a properly constrained kinematic linkage
 Determine the acceleration or force causing acceleration using Newton’s Second Law of Motion
 Determine the motion of kinetic systems using the principle of work and energy
 Determine the motion of particles using the principle of impulse and momentum
 Determine the forces acting on rigid bodies in motion
Prerequisites by Topic Course Topics
 Rectilinear motion of particles
 Relative and dependent motion of particles
 Curvilinear motion of particles
 Plane kinematics of rigid bodiesvelocities
 Plane kinematics of rigid bodiesaccelerations
 Kinematics of particlesNewton’s 2nd Law
 Work and energy
 Conservation of energy
 Impulse and momentum
 Kinetics of rigid bodies
Coordinator Dr. Joseph Musto



ME 207  Mechanics of Materials 3 lecture hours 2 lab hours 4 credits Course Description This is the first course in the mechanics of deformable bodies. Topics include stresses and strains produced by axial loading, torsion, and bending; elastic deflections of beams; effects of combined loading; and buckling of slender columns. Laboratory topics will reinforce lecture material. Not for credit for students who have credit in either AE 201 or AE 2011 . (prereq: ME 205 or ME 255, MA 231 or MA 2314 or MA 226) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Determine stresses resulting from axial, bending, torsion, and transverse loading
 Apply Hooke’s Law for materials with linear stressstrain behavior
 Construct shear and bending moment diagrams for statically indeterminate structures
 Determine the stress state in a member resulting from combinations of loads
 Know how to find principal stresses for a state of plane stress
 Determine beam deflections by integrating the moment equation
 Be familiar with the Euler buckling load for columns of various end conditions
Prerequisites by Topic
 Statics
 Integral and differential calculus
Course Topics
 Review of statics, reactions, internal loads
 Concept of stress and strain
 Mechanical properties of materials
 Axial loading
 Stress concentrations
 Torsion
 Shear and moment diagrams
 Bending stresses
 Transverse shear
 Combined loads
 Stress and strain transformations, including Mohr’s circle and strain rosettes
 Principal stresses
 Beam deflections
Laboratory Topics
 Specimen in tension or compression
 Uniaxial loading in a truss
 Shear of joined sections
 Combined stresses
 Stresses in beams
 Beam deflection
 Stressstrain curve
Coordinator Dr. Robert Rizza



ME 230  Dynamics of Systems 4 lecture hours 0 lab hours 4 credits Course Description This course introduces the modeling of electrical, mechanical, fluid and thermal engineering systems and the various methods for solving their corresponding differential equations. A systems approach is employed to represent dynamical systems and quantify their response characteristics. (prereq: EE 201 , MA 235 , ME 190 , and (ME 206 or ME 2002 )) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand basic system components of mechanical, electrical, thermal, and fluid systems and combine components into systems
 Formulate mechanical, electrical, thermal, fluid and mixed discipline systems into appropriate differential equation models
 Analyze linear systems for dynamic response  both time and frequency response
 Recognize the similarity of the response characteristics of various physically dissimilar systems
 Solve systems using classical methods and MATLAB/Simulink
Prerequisites by Topic
 Electrical circuits
 Differential equations
 Dynamics
Course Topics
 Introduction to dynamic systems
 Review of time domain solutions for 1st and 2nd order systems
 Free and constant force responses (step input)
 Determine system dynamic response characteristics
 Laplace domain analysis and polezero plots
 Block diagram model representation and transfer functions
 Simulation of block diagrams systems using Simulink
 Modeling mechanical systems (MSD)
 Modeling of mechanical systems (Torsional Systems)
 Linearization of differential equations
 Modeling electrical systems (RC and RLC circuits)
 Modeling of operational amplifiers
 Modeling of electromechanical systems (DC motor)
 Modeling of other analogous systems
 Statespace representation
 Numerical integration with Euler and ODE45
 Frequency response function
 Bode plots and 1st and 2nd order system characteristics
Coordinator Dr. Daniel Williams



ME 300  Modeling and Numerical Analysis 3 lecture hours 2 lab hours 4 credits Course Description This course is a study of mathematical techniques used to model engineering systems. It involves the development of mathematical models and the application of the computer to solve engineering problems using the following computational techniques: Taylor Series approximation, numerical differentiation, root finding using bracketing and open methods, linear and polynomial curve fitting, solution methods for matrix equations, numerical integration, and the solution of differential equations. Laboratory sessions involve the application of numerical analysis to physical systems involving statics, dynamics, fluid dynamics, heat transfer, electrical circuits, and vibratory systems. (prereq: ME 230 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Model engineering systems using first and second order differential equations, and solve the equations both analytically and numerically
 Employ the Taylor Series for approximation and error analysis
 Formulate and apply numerical techniques for root finding, curve fitting, differentiation, and integration
 Write computer programs to solve engineering problems
Prerequisites by Topic
 Programming
 Differential equations
 Differential and integral calculus
Course Topics
 Introduction to modeling
 Error analysis/Taylor Series
 Root finding
 Curve fitting
 Matrix applications
 Numerical differentiation
 Numerical integration
 Differential equations
 Partial differential equations & boundary value problems
Laboratory Topics
 Programming/computing techniques
 Matrix solution methods
 Solution of simultaneous equations
 Modeling and numerical simulation of first and second order mechanical/electrical/thermal systems
 Applications of rootfinding to vehicle dynamics & thermal insulation
 Applications of curvefitting to experimental data
 Applications of numerical integration to evaluate moments of inertia, friction work, volumetric fluid flow, and/or thermal heat flow
Coordinator Dr. Nebojsa Sebastijanovic



ME 314  Principles of Thermodynamics II 4 lecture hours 0 lab hours 4 credits Course Description This is a continuation of introductory thermodynamic concepts for mechanical engineering students. The course begins with energy balances for unsteady processes, followed by a detailed treatment of entropy and the second law of thermodynamics. Isentropic efficiency, irreversibility and exergy are covered. Thermodynamic principles are applied to the study of gas power cycles, vapor power cycles, and refrigeration cycles. Thermodynamic performance parameters are used to characterize the cycles, including a discussion of energy use and environmental impacts. (prereq: ME 311) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Write the energy balance for unsteady flow, and use it to evaluate processes, including determination of work and heat transfer
 Apply a Second Law analysis (entropy or energy) to processes involving both closed and open systems
 Evaluate the performance of Rankine and Brayton cycles, with their modifications
 Analyze refrigeration cycles
Prerequisites by Topic
 First Law of Thermodynamics
 Ideal gas, equation of state, steam tables, property diagrams
 Energy balances for closed and open systems
Course Topics
 Unsteady flow processes
 Second Law, entropy, reversible and irreversible processes, performance parameters of real and ideal devices, isentropic efficiency, exergy
 Rankine cycle with modifications
 Brayton cycle with modifications
 Refrigeration cycles
Coordinator Dr. Prabhakar Venkateswaran



ME 318  Heat Transfer 4 lecture hours 0 lab hours 4 credits Course Description This course covers the three fundamental mechanisms of heat transfer: conduction, convection, and radiation. The course includes steady state and transient conduction, free and forced convention, as well as heat exchanger design. (prereq: ME 2101 , ME 3103 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Demonstrate the ability to model physical systems subject to heat transfer, using calculus and differential equations
 Demonstrate the ability to solve the related differential equations, and concretely relate the results to observable heat transfer processes
 Apply models of conduction, convection and radiation heat transfer, and to solve practical engineering heat transfer problems
Prerequisites by Topic
 Fluid mechanics
 Differential equations
 1st Law of Thermodynamics
Course Topics
 Introduction to heat transfer (rate laws for the three heat transfer mechanisms)
 The heat diffusion equations
 Onedimensional steadystate conduction for planar, cylindrical, and spherical geometry
 Electrical circuit analogy to heat transfer analysis
 Fins
 Transient lumped capacitance method
 Physical significance of dimensionless parameters
 Forced convection (external flow)
 Forced convection (internal flow)
 Free convection
 Heat exchangers
 Radiation overview
Coordinator Dr. Christopher Damm



ME 321  Materials Science 3 lecture hours 0 lab hours 3 credits Course Description Atomic, crystal and defect structure fundamentals are studied to lay the foundation for understanding the structurepropertyprocessing relationship. Material properties (with particular focus on mechanical properties) are described along with common test methods. (prereq: CH 201 ) (coreq: ME 2004 or ME 207 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Classify materials based on structure and bonding
 Be familiar with common mechanical properties of materials and testing methods
 Be familiar with the fundamental crystal structures and important crystallographic defects of various materials
 Be familiar with the fundamentals of atomic movement in solids, including how it occurs and the mathematical models
 Be familiar with typical properties and common engineering applications of broad categories of materials (metals, polymers, ceramics, composites)
 Be familiar with engineering literature/resources for material property information
Prerequisites by Topic
 Introductory solid state chemistry
 Introductory strength of materials
 Differential/integral calculus
Course Topics
 Types of materials (metals, ceramics and polymers) and the structurepropertyprocessing relationship
 Properties of materials, sources of material property data, standards for testing, relative property values for the major classes of materials
 Mechanical and physical properties of materials (metals, ceramics and polymers)
 Bonding and structure in materials (metals, ceramics and polymers), including defects and imperfections
 Atomic movement (diffusion) in crystalling solids
 Ceramics and ceramicmatrix composites
 Polymers and polymermatrix composites
Coordinator Dr. Cynthia Barnicki



ME 322  Engineering Materials 3 lecture hours 2 lab hours 4 credits Course Description The course covers the relationship between structure, properties, and processing in engineering material. The primary emphasis is on metals. Basic concepts of solidification and heat treatment are presented. Alloy phase diagrams and lever rule calculations are shown as a means to understanding both solidification and heat treatment. The relationship between processing/heat treatment and the underlying related strengthening mechanisms are presented. Material selection in terms of mechanical strength service stability, cost, and environmental impact are discussed. (prereq: ME 321 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Utilize binary alloy phase diagrams in microstructure determination and heat treating
 Apply knowledge of the structure processingproperty relationships to specify basic heat treatment, solidification, and deformation processes to obtain desired properties
 Identify important microstructural features in various alloy systems
 Be familiar with typical mechanical properties and applications of common alloys
 Be familiar with basic materials lab equipment and conduct experiments
 Correctly analyze and interpret data from lab experiments
Prerequisites by Topic
 Atomic, crystal and defect structure in solids
 Atomic movement in solids, diffusion
 Structure and general properties of metals
 Strength of materials
 Introductory thermodynamics
Course Topics
 Review of mechanical properties
 Overview of strengthening mechanisms in metals and alloys
 Deformation of metals and strain hardening
 Principles of solidification
 Isomorphous phase diagrams and phase rule
 Eutectic phase diagrams and solidification in eutectic systems
 Precipitation hardening
 Microstructure and heat treatment of steels
 Martensite transformation, tempering
 Effect of alloy elements in steels
 Stainless steels
 Cast iron
Laboratory Topics
 Hardness testing
 Metallographic methods
 Recrystallization of brass
 Impact testing
 Cooling curves/PbSn phase diagram
 Precipitation strengthening of aluminum
 Heat treatment of steel
 Jominy test/hardenability of steel
 Tensile testing
Coordinator Dr. Cynthia Barnicki



ME 323  Manufacturing Processes 3 lecture hours 2 lab hours 4 credits Course Description This course covers the basic manufacturing processes commonly used in the production of metal, plastic, and composite parts. Process description, product/process characteristics are covered along with design and economic and environmental considerations. Topics include casting, forging, powder metallurgy, traditional machining, laser/waterjet/EDM, welding, additive manufacturing, and various processes for producing polymer parts. The course introduces several topics in manufacturing systems including computer simulation of the casting process, design for manufacturing, quality control, and sustainable manufacturing. (prereq: ME 322 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Describe the attributes of common manufacturing processes
 Understand the advantages and limitations of common manufacturing processes
 Recommend a manufacturing process based on characteristics of a part and required production quantities
 Design components for ease of manufacture
Prerequisites by Topic Course Topics
 Attributes of manufacturing systems
 Casting processes
 Powder metallurgy
 Deformation processing
 Machining  traditional metal cutting
 Nontraditional machining  EDM, laser and waterjet
 Welding
 Design for manufacturing and assembly
 Sustainable manufacturing & recycling
 Polymer part processing
 Additive manufacturing
Laboratory Topics
 Measurement and statistical process control
 Introduction to SolidCast©  simulating the sand casting process
 Using SolidCast© to design a sand cast mold
 Foundry practice and sand casting
 CNC machining
 Product reverse engineering to determine manufacturing process
 Surface roughness measurement
Coordinator Dr. Matt Schaefer



ME 354  Thermodynamics and Heat Transfer 3 lecture hours 0 lab hours 3 credits Course Description A study of the fundamental concepts and laws of heat transfer, with supporting foundation in thermodynamics. Application of principles of heat transfer to problems encountered in electrical and computer equipment. Not for ME majors. (prereq: MA 226 or MA 231 or MA 2314 and PH 2030 or PH 2031 or ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Apply mass and energy balances to simple thermodynamic systems
 Apply heat transfer equations to solve problems in cooling of electronic and electrical components or other applicable problems
Prerequisites by Topic
 Introductory thermal physics
Course Topics
 Introduction to thermodynamic analysis: system, property, process
 Mass and energy balance equations
 Ideal gas equations of state
 Energy balance for closed and open systems
 Heat transfer mechanisms: introduction
 Conduction
 Convection: forced and natural
 Radiation or heat exchangers (instructor’s choice)
Coordinator Dr. Christopher Damm



ME 362  Design of Machinery 3 lecture hours 0 lab hours 3 credits Course Description This course is an application of principles of machine dynamics to the design of machinery. Topics include synthesis of mechanisms, machine balancing, design of flywheels, actuator selection and computeraided design of mechanisms. (prereq: ME 2003 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Synthesize four bar linkages
 Apply computeraided engineering packages to machinery design
 Determine the actuation force or torque required for a mechanism, and select an appropriate actuator
 Determine shaking forces due to dynamic unbalance, and perform static and dynamic balancing
 Design flywheels
 Perform dynamic analysis of cam/follower systems
Prerequisites by Topic Course Topics
 Fundamentals of dynamics
 Practical considerations, actuators and motors
 Computeraided engineering
 Linkage synthesis
 Machine balancing
 Design of flywheels
 Dynamics of cams
 Project presentations
Laboratory Topics Coordinator Dr. William Farrow



ME 363  Design of Machine Components 4 lecture hours 0 lab hours 4 credits Course Description This course applies mechanics of materials concepts to the design of machine components. Static and fatigue failure criteria are introduced and applied to shafts, bearings, gears, threaded fasteners, and helical springs. (prereq: ME 3005 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Calculate factors of safety for ductile and brittle components subjected to static and cyclic loading
 Be familiar with terminology associated with various machine components
 Design or select shafts, journal and rollingelement bearings, spur and helical gears, threaded fasteners, and helical springs
Prerequisites by Topic
 Mechanics of materials
 Dynamics of machinery
Course Topics
 Static design
 Traditional tolerances
 Review of static and fatigue failure criteria
 Shafts, including keys and keyways
 Rollingelement bearings
 Spur gears
 Helical gears
 Threaded fasteners
 Helical springs
Coordinator Dr. Robert Rizza



ME 401  Vibration Control 3 lecture hours 0 lab hours 3 credits Course Description This is an introduction to mechanical vibrations, to free and forced vibrations of singledegree of freedom systems, and to twodegree of freedom of systems. Various types of forcing functions are considered for both damped and undamped systems. (prereq: MA 232 or MA 2323 , ME 230 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Model simple vibratory systems and determine equations of motion
 Solve equations of motion for single degree of freedom systems subject to harmonic, general periodic and arbitrary forcing functions
 Write equations of motion for idealized multidegree of freedom systems
 Determine natural frequencies and mode shapes for systems with two and three degrees of freedom
 Develop appropriate analytical models for simulation using MATLAB w/ Simulink
 Perform measurements and conduct modal tests on simple systems
Prerequisites by Topic
 Dynamics
 Calculus
 Differential equations
 Computer programming
Course Topics
 Review: Modeling mechanical systems
 Review: Solving differential equations  analytical, numerical methods
 Free vibration
 Harmonically excited vibration
 Fourier series, periodic functions
 Transient vibration
 Systems with two or more degrees of freedom
 Lagrange’s equation
 Vibration control
 Vibration measurement and applications
Laboratory Topics
 Free and forced vibration demonstration and measurement on 1 and 2 DOF systems
Coordinator Dr. Subha Kumpaty



ME 402  Vehicle Dynamics 3 lecture hours 0 lab hours 3 credits Course Description This course covers the application of engineering mechanics to the design of road vehicles. Topics include pneumatic tires, load transfer, performance limits, suspension and steering, and handling and response. (prereq: ME 230 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Simulate acceleration and braking performance of common vehicles
 Model the normal road loads acting on vehicles
 Model and simulate suspension forces due to road inputs and steady state cornering forces
 Design and simulate common suspension and steering geometries
 Apply tire properties to vehicle performance
Prerequisites by Topic
 Kinematics
 Dynamics of systems
Course Topics
 Introduction to modeling and dynamic loads
 Power and traction limited acceleration models
 Braking performance, forces, and systems
 Road loads, aerodynamic drag, and rolling resistance
 Ride and suspension models
 Steady state cornering, forces, and suspension effects
 Analysis of common suspensions
 Analysis of common steering systems
 Properties and construction of tires
 Safety ratings and rollover propensity
Coordinator Dr. Nebojsa Sebastijanovic



ME 409  Experimental Stress Analysis 2 lecture hours 2 lab hours 3 credits Course Description In this course, students learn to apply modern experimental stress analysis techniques to measure strains and stresses in engineering components and structures. The course includes strain gage measurements and analysis, design of strain gagebased transducers, photoelasticity, and stress analysis. (prereq: ME 3005 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand concept of stress and strain
 Understand underlying principles in using strain gages
 Mount strain gages, take measurements, and analyze the obtained data
 Design strain gagebased transducers for measuring specific loads
 Understand basic principles of photoelasticity and use it as an analysis tool
 Use sources outside the class notes and text
Prerequisites by Topic
 Intermediate Mechanics of Materials
Course Topics
 Review of states of stress
 State of strain at a point
 Principal strains and Mohr’s circle
 Electrical Resistance strain gages
 Strain gage circuits
 Transducer design
 Photoelasticity
Laboratory Topics
 Strain measurement on a cylindrical pressure vessel
 Strain gage mounting practive
 Strain gage mounting and soldering
 Strain measurements of lab projects
 Photoelasticity demonstration
 Photoelastic measurement
Coordinator Dr. Mohammad Mahinfalah



ME 411  Advanced Topics in Fluid Mechanics 3 lecture hours 0 lab hours 3 credits Course Description This course focuses on differential relations for treating fluid flow problems. The theory developed will allow students to pursue advanced practice in fluid dynamics (e.g. computational fluid dynamics). In addition to differential relations and potential flow theory, this course covers dimensional analysis/similitude, and external flow. The NavierStokes equations are applied to fluid mechanics problems both analytically and numerically. (prereq: ME 3104 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Characterize potential flow fields
 Analyze certain types of flows using NavierStokes equations
 Use numerical analysis to solve potential flow problems
Prerequisites by Topic
 Introductory fluid mechanics
 Vector calculus
 Differential equations
 Partial derivatives
Course Topics
 Differential analysis of fluid flow
 Fluid element kinematics
 Differential forms of conservation of mass, momentum, and energy equations
 Potential flow
 Stressdeformation relationships for viscous flow
 The NavierStokes equations
Coordinator Dr. Christopher Damm



ME 419  Internal Combustion Engines 2 lecture hours 2 lab hours 3 credits Course Description This course covers the basic theory of internal combustion reciprocating engines. Course topics include engine performance parameters, combustion, engine cycles, fuels, and emissions. (prereq: ME 3105 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand the general engineering operation and design compromises involved in spark and compression ignition engines
 Be familiar with common I.C. engine terminology such as knock, detonation, auto ignition, surface to volume ratio, and compression ratio
 Apply thermodynamics to I.C. engine processes and cycles
 Analyze the engine parameters of friction, torque, MEP, IHP, and bsfc
 Understand the mechanisms of combustion and the effect of airfuel ratio on performance
 Understand the variables which influence the production of undesirable emissions
 Understand the importance of air flow and how it is affected by valves and by forced induction (turbocharging and supercharging)
Prerequisites by Topic
 Thermodynamic cycles and processes
 Combustion chemistry
Course Topics
 Engine types and operation
 Engine parameters
 Engine power cycles
 Inlet and exhaust gas flow
 Combustion  SI engines
 Combustion  CI engines
 Emissions and control
Coordinator Dr. Christopher Damm



ME 423  Materials Selection 3 lecture hours 0 lab hours 3 credits Course Description This course provides students with an understanding of materials as grouped systems, as well as familiarization with enough specific engineering materials to allow their effective use in daily assignments. The course also illustrates guidelines for screening candidate materials and arriving at reasonable choices. (prereq: ME 323 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Optimize material and shape selection factors
 Screen candidate materials and select suitable choices to fit given application requirements
Prerequisites by Topic
 Mechanical properties
 Strength and materials
 Heat treatment and properties of ferrous alloys
 Heat treatment and properties of aluminum alloys
 Polymer basics
 Manufacturing processing for metals, polymers, & composites
Course Topics
 Categorization of materials and processes
 Design process and materials selection
 Identification of design functions constraints and objectives
 Screening selection with multiple constraints
 Influence of shape
 Product characteristics
Coordinator Dr. Matt Schaefer



ME 424  Engineering with Plastics 3 lecture hours 0 lab hours 3 credits Course Description This course provides students with knowledge of polymers that are commonly used and of how the physical and mechanical properties of these materials influence their selection. Also, the relation between fabrication processes and material selections in design is presented. (prereq: ME 321 or equivalent) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Know fundamentals of redesigning a metal part using a polymer
 Know the fundamental mechanical properties of polymers
 Interpret resin manufacturer’s data sheets
 Analyze components and structures fabricated from polymers from a mechanical design viewpoint
 Predict the mechanical performance of parts fabricated from polymers and composites
 Select the most desirable manufacturing process and a suitable polymer for producing a given component
 Be familiar with ASTM test standards
Prerequisites by Topic
 Mechanical & physical properties of materials
 Basic mechanics of materials
Course Topics
 Classification and description of polymers
 Properties of polymers
 Processing of polymers
 Polymer design criteria and considerations
 Applications of polymers (such as creep, wear, friction, damping, etc.)
 Fiberreinforced composites, macroscopic composites
 Structural and component analysis
Coordinator Dr. Cynthia Barnicki



ME 429  Composite Materials 3 lecture hours 0 lab hours 3 credits Course Description This course introduces the student to the mechanical behavior of fiberreinforced composite materials. Topics to be covered include anisotropic stressstrain relationships, failure theories, and stress analysis of plates and shells. Different manufacturing methods and applications will be presented. (prereq: ME 2004 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Be familiar with indicial notation
 Transform tensor quantities from one coordinate system to another
 Compute stresses and strains for a composite lamina subjected to inplane, bending, and thermal loads
 Apply different failure criteria to predict laminate failures
 Be familiar with the most commonlyused manufacturing processes of composite structures
 Be familiar with aerospace, automotive, recreational, and industrial applications of composite materials
 Be familiar with several standard test methods of composites
Prerequisites by Topic Course Topics
 Introduction to composite materials
 Indicial notation, matrices, and tensors
 Mechanics of a composite lamina
 Extensional behavior of a symmetric laminate
 Failure criteria
 Composite schedules
 Macro mechanical models
 Micro mechanical models
 Mechanical behavior of a general lamina
 Manufacturing processes
 Test methods
 Testing lab demonstration
Coordinator Dr. Robert Rizza



ME 431  Automatic Control Systems 3 lecture hours 2 lab hours 4 credits Course Description This course provides an introduction to automatic controls used in mechanical engineering applications, including fluid power. Differential equations are used to model and analyze basic feedback control systems. Laboratory experiments are done using fluid power and electronic equipment. (prereq: ME 230 ) (coreq: ME 300 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Use Laplace transformation and selected linearization techniques
 Develop mathematical models of selected systems
 Determine system stability using the Routh and root locus techniques
 Determine steady state errors due to reference and disturbance inputs
 Make root locus plots and use them as appropriate to evaluate system transient response characteristics
 Construct and analyze Bode plots
Prerequisites by Topic
 Differential equations
 System dynamics
Course Topics
 Introduction
 Mathematical models of systems
 State variable models
 Feedback control systems characteristics
 The performance of feedback control systems
 The stability of linear feedback systems
 The root locus method
 Frequency response methods
 Stability in the frequency domain
Laboratory Topics
 Laboratory orientation
 RLC step input modeling
 RLC dynamic measurements
 Valve steady state PQ characteristics
 Dynamic valve characteristics
 Rotary speed control simulation
 Rotary speed control
 Position control
Coordinator Dr. Daniel Williams



ME 433  Electromechanical Systems 3 lecture hours 2 lab hours 4 credits Course Description This course extends the concepts of feedback control to the design and realization of electromechanical systems. Topics will include modeling, simulation, and implementation of digital control algorithms. The course includes an electromechanical systems design project. (prereq: ME 431 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Develop mathematical models of electromechnical components and systems
 Evaluate and select sensors and electrical circuit components
 Formulate and evaluate analog and digital controllers
 Specify and evaluate state feedback algorithms
 Design an electromechanical system to achieve specified performance objective
 Determine component and systemwide frequency response characteristics
 Develop frequency response design tools
Prerequisites by Topic
 Laplace transforms
 Feedback control systems
 Numerical methods
Course Topics
 DC motor modeling
 Analog component selection
 Ztransforms
 Difference equations
 State feedback
 Digital system effects
Laboratory Topics
 Electric motor characteristics
 Discrete equivalent PID controller implementation
 Electromechanical design and simulation
Coordinator Dr. Daniel Williams



ME 460  Finite Element Methods 3 lecture hours 2 lab hours 4 credits Course Description This course serves as an introduction to finite element analysis (FEA) for structural and steadystate thermal problems. In the lecture portion of the course, finite element equations are developed for several element types from equilibrium and energy approaches and used to solve simple problems. In the laboratory portion, students use a commercial, generalpurpose finite element computer program to solve more complex problems and learn several guidelines for use of FEA in practice. A project introduces the use of FEA in the iterative design process. (prereq: ME 309 or ME 3005 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Understand steps involved in FEA analysis
 Understand how finite element equations are developed from both equilibrium and energy methods
 Solve simple FE problems by hand
 Understand why certain element types are used for different types of analyses
 Be familiar with the use of a commercial generalpurpose FEA package
 Understand how FEA can be used in the design process
Prerequisites by Topic
 Mechanics of materials
 Statics
 Integral and differential calculus
Course Topics
 Overview of method
 Overview of commercial software
 Review of matrix methods
 Spring elements
 Truss elements
 Potential energy approach
 Beam element
 Constant strain triangle element
 Heat transfer application
 Interpretation of results & mesh design
 Discussion of symmetry and boundary conditions
 Advanced element formulations
Laboratory Topics
 Introduction to FE program (with simple 1D truss element)
 Stress concentration in a plate with a hole
 3D truss analysis
 1D cubic beam bending of a frame analysis
 Plane stress analysis with twodimensional continuum elements
 Plate analysis
 Mesh design & refinement
 2D steadystate heat transfer, thermal analysis and/or torsion
 Solid modeling input to FE commercial software
 Design project
Coordinator Dr. Nebojsa Sebastijanovic



ME 480  HVAC Systems Design 2 lecture hours 2 lab hours 3 credits Course Description This course explores major elements in the design of heating, ventilating, and air conditioning systems. Topics include psychrometric analysis, load estimation, duct/piping design, equipment selection, and energy consumption estimating. The Carrier building simulation software is utilized. Students are required to design elements of HVAC systems, resulting in an understanding of the entire process. (prereq: none) (coreq: ME 3105 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Do a heating and cooling load calculation for a building
 Evaluate the psychrometric processes involved in heating and cooling a building
 Make appropriate choices for heating and cooling equipment
 Utilize a commerciallyavailable software package (Carrier E20II) to simulate the HVAC system for a building
Prerequisites by Topic Course Topics
 Psychrometric analysis
 System types
 Heating and cooling load analysis
 Air distribution and duct sizing
 Air system acoustics
 Water systems
 Equipment and control system selection
 Supervised Design Project work
Coordinator Dr. Michael Swedish



ME 481  Aerodynamics 3 lecture hours 0 lab hours 3 credits Course Description Reviews nondimensional numbers and boundary layer concepts. Covers a physical description and understanding of fluid flow over bluff and streamlined bodies; experimental and theoretical lift and drag results for both twodimensional and finite airfoils; aircraft stability and control; propeller design; automobile aerodynamics, including airfoil, spoilers, and airdams. (prereq: ME 3104 ) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Have a thorough understanding of fluid flows over bluff and streamlined bodies, including potential flow results, circulation, boundary layers, transition, and experimental results
 Choose an airfoil and apply lift, drag, and moment coefficients to a design and to be able to measure these coefficients experimentally
 Make thin airfoil and finite airfoil calculations
 Make airplane stability and trim calculations
 Have an introduction to automobile aerodynamics
Prerequisites by Topic
 Incompressible flow, Bernoulli equation
 Laminar and turbulent flows, Reynolds number, viscosity
 Boundary layers
 Integral calculus
Course Topics
 Review of fluids, nondimensionalization, boundary layer, friction
 2D flow over cylinders and airfoils
 Movies and laboratory experiments
 Airfoil terminology, characteristics, and physical flow description, modern airfoil developments, high lift devices
 Thin airfoil theory
 Finite airfoil
 Stability and control
 Propellers, vortex motion, model airplanes
 Automotive applications
Laboratory Topics
 Wind tunnel measurements of formula car drag coefficient and airfoil lift, drag, and moment coefficients and instrumentation
Coordinator Dr. Christopher Damm



ME 485  Energy Systems Design Project 3 lecture hours 0 lab hours 3 credits Course Description This course involves the application of energy principles to an engineering design problem. A project with practical application is chosen, with an emphasis on resource conservation. (prereq: ME 318 or ME 354 or equivalent) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Utilize a design methodology, including creative synthesis of solutions; evaluation of solutions based on criteria and constraints; sensitivity analysis; choice of “best” design
 Work effectively as part of a team
 Work with deadlines
 Communicate ideas
 Defend his/her decisions
Prerequisites by Topic
 Thermodynamics
 Fluid mechanics
 Heat transfer
Course Topics
 Outline of design process; project assignments
 Problem statement
 Literature search techniques
 Brainstorming/list of solutions
 Criteria and constraints/criterion function
 Sensitivity analysis
 Oral presentation guidelines
 Report writing guidelines
 Oral presentations
 Team meetings with instructor
 Team project work
Coordinator Dr. Michael Swedish



ME 490  Senior Design I 3 lecture hours 0 lab hours 3 credits Course Description This course functions as the proposalwriting phase for the major design experience in the Mechanical Engineering Program. Student design teams are organized and paired with a faculty advisor. A detailed design proposal is prepared. Topics covered in lectures and addressed in the design proposal include the design process, engineering specifications, patents and intellectual property, library research techniques, reliability and safety, design for manufacturability, and project management. (prereq: senior standing) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
 Have written a detailed design proposal for the major design experience
 Have researched trade and professional literature, patents, codes, and specifications related to the topic of the design proposal
 Have made an oral presentation of proposed design efforts to the advisors
 Have addressed possible societal and environmental impacts of their project
Prerequisites by Topic
 None, although students are required to select a project for which they have sufficient expertise
Course Topics
 Team formation and project expectations
 The design process
 Work place safety
 Patents and intellectual property
 Library research
 Project management
 Reliability and safety
 Design for manufacturability
 Proposal preparation
 Professional development
Coordinator Dr. Mohammad Mahinfalah


Page: 1 < 2
 3
 4
 5
 6
 7
 8
 9
 10
 11
 12


