Jul 23, 2024  
2024-2025 Graduate Academic Catalog-June 
2024-2025 Graduate Academic Catalog-June

Machine Learning, M.S.

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Program Director

RJ Nowling, Ph.D.
Office: DH-421
Phone: (414) 277-7318
Email: nowling@msoe.edu


The Master of Science in Machine Learning program is an online program geared towards students who wish to develop advanced skills in machine learning systems development and deployment. The program provides students with the skills necessary to develop and deploy machine learning solutions in their technical fields. The program is open to students who have a bachelor’s degree in a technical field and significant programming experience. Key features of this program include depth of technical content, industry applications and integration in every course, access to ROSIE (MSOE’s GPU cluster), small class sizes, and faculty who excel in teaching, research, and student support.  

MSOE has distinguished itself as a leader in the area of Machine Learning (ML) with the first undergraduate Computer Science program focused on applications of artificial intelligence. The M.S. in Machine Learning supports students who wish to develop the advanced technical skills needed to create new products integrating ML and big data.  While there are many post-baccalaureate programs that introduce students to the concepts of ML and touch on areas such as natural language processing and computer vision, few if any programs are geared towards the application of ML to industrial problems and the development and deployment of ML-based products. This 32-credit program provides the depth of content necessary to develop ML-based solutions. It leverages the student’s existing skills in programming and application area knowledge to dive right into advanced concepts that can be applied immediately. 

The program is open to both full-time and part-time students. The eight required courses can be taken at a pace of one or two courses per semester, including summer. Five-year BS/MS paths through several MSOE undergraduate programs provide another option for completion.

Program Educational Objectives

The Machine Learning Master’s program will prepare graduates, within a few years of graduation, to:

  1. Be the lead architect on complex projects involving machine learning and data science
  2. Develop solutions that address competing ethical and professional concerns as both technology and society continue to evolve
  3. Pursue continued technical and professional development

Student Outcomes

  1. Analyze complex problems involving advanced applications of machine learning and data science and design solutions that meet relevant business, technical, and ethical standards
  2. Apply a rigorous, scientific approach that includes forming research questions, generating hypotheses, designing and executing experiments, and evaluating results to make informed judgements
  3. Effectively evaluate and utilize state-of-the-art software and parallel computing hardware in the design and implementation of projects
  4. Effectively describe solutions and their implications and communicate results to technical and non-technical audiences
  5. Successfully deploy production-quality solutions involving machine learning and data science techniques using current best practices


Dr. Sebastian Berisha, Dr. John Bukowy, Dr. Eric Durant, Dr. Jonathon Flynn, Dr. Jeremy Kedziora, Dr. RJ Nowling, Dr. Jay Urbain, Dr. Josiah Yoder

Machine Learning, M.S. Version S1

Program total: 32 credits


  • CSC 5610 may be substituted with a 5000-level elective for students with appropriate recent course credit or experience, including students who have completed MSOE’s CSC 2611 and CSC 2621 courses.
  • CSC 5610 is met by substitution by students who have completed both BUS 6121 and BUS 6131.
  • MTH 5810 is substituted with a 5000-level elective for students who have both linear algebra background comparable to MSOE’s MTH 2340 and multivariable calculus background comparable to MSOE’s MTH 2130. Early entry students must take the undergraduate versions of these courses.

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