Mar 14, 2026  
2026-2027 Graduate Academic Catalog 
    
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2026-2027 Graduate Academic Catalog

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

Overview

The Master of Science in Machine Learning program is an online program geared towards students who wish to develop advanced skills in machine learning (ML) and artificial intelligence (AI). The program is geared towards student with technical and engineering backgrounds and aims to provide unprecedented depth to its students.  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 covers both the development of models as well as integration, deployment, and management of models in production software systems. Through elective choices, students can develop specializations and earn graduate certificates in Generative AI Production Systems, ML Engineering, and Tiny Machine Learning.

The program accepts students who have a bachelor’s degree in a technical field and previous 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, faculty who excel in teaching, research, and student support, and online, synchronous evening courses.

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.

Faculty

Dr. Sebastian Berisha, Dr. John Bukowy, Dr. Jonathon Flynn, Dr. Jeremy Kedziora, Dr. Richard Lukas, Jr., Dr. RJ Nowling, Dr. William Retert, Dr. Jay Urbain, Dr. Josiah Yoder

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

Machine Learning, M.S.

Version S2

Required Courses

Students complete all seven courses below for a total of 28 credits.

Electives

Students choose at least one elective from the list below.

Program total: 30-32 credits

Notes:

  • CSC 5201 may be substituted with CSC 6711  or CSC 6712  for students with recent relevant course credit or experience. A different elective may substituted with program director approval.
  • CSC 5610 may be substituted with an elective for students with appropriate recent course credit or experience, including students who have completed both CSC 2611 and CSC 2621 or both BUS 6121  and BUS 6131 .
  • MTH 5810 may be substituted with an elective for students who have background comparable to MTH 2810 Linear Algebra and Optimization or both MTH 2340 Linear Algebra with Applications and MTH 2130 Calculus III. Early entry students must take MTH 2810 or both MTH 2340 and MTH 2130.
  • CSC 6605 may be substituted with CSC 6607  or CSC 6608 .  A different elective may substituted with program director approval.
  • CSC 7901 may be substituted with an elective.
  • PHL 6001 may be substituted with an elective if students have taken PHL 3102 at MSOE.

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