Oct 14, 2019  
2017-2018 Undergraduate Academic Catalog 
    
2017-2018 Undergraduate Academic Catalog [ARCHIVED CATALOG]

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EE 488 - Introduction to Artificial Intelligence and Expert Systems

3 lecture hours 0 lab hours 3 credits
Course Description
The objective of this course is to provide the student with an overview of topics in the field of artificial intelligence (AI). The course also provides the student with a working knowledge of designing an expert system and applying expert system technology in designing and analyzing engineering systems. The first part of the course covers historical background, knowledge acquisition and knowledge representation including propositional calculus, predicate calculus, semantic networks, frame systems and production rules. Various search techniques will be discussed. Fuzzy logic systems, neural network systems and computer vision systems will be briefly discussed in the second part of the course. Languages for AI problem solving such as Prolog and/or LISP will be introduced. The third part of this course will be devoted to the design of expert systems. Applications of expert systems in engineering system design and analysis will be stressed throughout. Case studies will be discussed. Class project is required. Students are encouraged to design expert systems for his/her own engineering applications, and an expert shell will be used to implement the design. (prereq: senior standing in CE, EE, or SE)
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
  • Represent knowledge using propositional calculus and predicate calculus
  • Use inference rules to produce predicate calculus expression
  • Solve problems using search techniques: depth-first, breadth-first, forward chaining, backward chaining, best-first, branch-and-bound, and-or-graph, and heuristic search
  • Analyze and design a fuzzy logic system using fuzzy logic tool box
  • Analyze and design a neural network system using neural network toolbox
  • Analyze and design a rule-based expert system
  • Design a machine vision system application

Prerequisites by Topic
  • Working knowledge of a high level computer language
  • Digital logic
  • Fundamental technical courses in the student’s major field

Course Topics
  • Introduction (1 class)
  • AI: History and Applications (1 class)
  • Knowledge Representation (5 classes)
  • Methods of Inference (1 class)
  • Search Techniques (3 classes)
  • Fuzzy Logic Systems (3 classes)
  • Neural Network (3 classes)
  • Pattern Recognition and Computer Vision (3 classes)
  • Expert Systems (6 classes)
  • Languages For AI Problem Solving (1 class)
  • Project presentation (1 class)
  • Review and Exams (5 classes)

Coordinator
Richard Kelnhofer



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