Master of Science in Machine Learning


The MS in Machine Learning program was first offered in fall of 2023, coinciding with MSOE’s transition to a semester system. It consists of 8, 4-credit courses.

All required courses are available online (fully virtual or in classroom with technology enabling remote participation). Many courses will be synchronous. The 4 classes that are part of graduate certificates (see track below) will be offered in the evening.

Students who were in the 2021–22 cohort of our applied ML graduate certificate, upon which the core classes in the MSML are based, were generally working full-time and said that it was better for them to take 1 course at a time instead of 2. So, for the master’s degree, that would translate to 3+ years depending on how many summer classes are taken. The program could be done in 2 years or a bit less if someone took 2 courses at a time.

We expect that many of the students in the program will have tuition support from their employers. Depending on the company, taking one course at a time might also be the best path from a financial perspective if the employer has annual limits on tuition support.

Note that the MSML program (and our ML graduate certificates) do not require letters of recommendation, although they are listed as optional on MSOE’s common application. Admission is strictly based on academic background and we personally reach out to applicants as needed to clarify any academic background that may be missing.

Please contact MSML program director Dr. Durant with any questions.

Dual Enrollment with Bachelor’s Degree

The MS can be taken as a 5-year early entry dual enrollment program with many MSOE undergraduate degrees including all EECS undergraduate degrees (BME, CE, CS, EE, and SE), AE, and CVE. Students must apply while they have junior standing, but should let their advisor know of their intent as early as possible.

4+1 Model Tracks for AE, BME, CE, CS, CVE, EE, SE

General 4+1 / Early Entry Policies Including Tuition

8 MB PowerPoint Overview Presentation on MSOE ML Programs

Official Websites




Landing / Information Request


Please see MSOE’s Accreditations for information accreditation at the university and program levels, including accreditation of all undergraduate programs in the EECS Department. Note that as of 2023 ABET does not offer offer accreditation for masters programs in any computing disciplines.

Machine Learning Careers In The News

Direct Admit Requirements (also apply to Graduate Certificate in Applied Machine Learning)


Required Courses

The 8 required courses comprise 2, 2-course subsets that each meet the requirements for a graduate certificate, plus 4 additional courses. Key prerequisites are noted here; more detailed prerequisites are noted below and in the graduate catalog.

Early entry students should take both MTH2130 Calculus III and MTH2340 Linear Algebra with Applications and replace MTH5810 with an approved elective.

MSOE CS students and any student who has completed the data science sequence through CSC2621 replace CSC5610 with an approved elective.

MSML students may have either or both of the certificates conferred when they meet the course requirements by selecting “Completion of Certificate Program” on the registrar’s website. MSOE early entry students are generally not eligible for this since MSOE does not allow a course to be counted for more than 2 credentials and these students are typically using the MSML courses to also meet an undergraduate degree requirement.

Example Plan

2-year, 5-semester (including 1 summer) flowchart with key prerequisites


As each student enters the program, the program director prepares a draft advising plan of which semester the student will take each course. Please contact the program director if you need a copy of your plan, and to make changes, such as increasing or decreasing how many classes you plan to take each semester.

Grade Replacement

MSOE’s machine learning programs automatically grant requests to retake and grade replace classes. This means that students in machine learning programs may re-enroll in a class without explicit permission. It also means that, if a grade below a B was earned the first time, the second grade can replace the original grade in GPA calculations. To take advantage of this option, students must notify their program director or coordinator. They will forward the request to the registrar’s office, which will then process the replacement at the end of the term. This must be done between week 2 and the week before exam week, preferably early in the term.

Notes on Specific Courses

Quarter System Applied ML Graduate Certificate (2021–’22 and 2022–’23)

Completing this certificate at MSOE (CS6230, CS6330, CS6340) meets the CSC5610 and CSC6621 requirements by substitution, reducing the number of additional courses needed to earn the MSML.

Graduate Certificate in Advanced Business Strategy Using AI and Analytics

The combination of BUS6121 Data Wrangling and Exploration and BUS6131 Predictive Analytics meets the CSC5610 requirement by substitution in the MSML.

The 3 credits in BUS6141 Analytics Leadership and Strategy can be used as MSML elective credits.

CSC5201 Microservices & Cloud Computing

Substitutions Allowed: Graduate Courses

There are opportunities to substitute certain courses for the CSC5201 requirement in the MSML and in the ML Engineering Graduate Certificate. CSC5201, CSC6711, or CSC6712 can satisfy the CSC5201 requirement. If MSML students take more than 1 of these classes, the additional courses are counted as MSML electives.

Substitutions Allowed: Undergraduate Courses

Students with sufficient coursework in both web apps and cloud computing replace this course with an approved elective.

Acceptable Web Apps Coursework

Acceptable Cloud Computing Coursework

Note: SWE2410 Design and Cloud Patterns is not sufficient to meet this requirement.

CSC5601 Theory of Machine Learning: CS Major and MSML Elective

Early entry CS majors have the option of taking this course to simultaneously satisfy their undergraduate requirement of CS3400 or CSC4601. Students who have credit for CS3400 or CSC4601 may not take CSC5601.

CSC5980 Special Topics: An Engineering Approach to Game Theory and Economics

For decades, the study of strategic interactions between rational decision makers has formed the basis of game theory. This course, using a two-volume textbook, extends game theory concepts to focus on dynamic games, introducing students to a new take on game theory referred to as the field theory of games. The course prioritizes conceptual understanding over mathematical equation solving, making the text accessible to not only engineering students but also to a more general audience, including business students.

By using a toolkit based on the Wolfram Language, students can bypass the need to solve linear programming problems and partial differential equations by hand, allowing them to arrive at solutions with practical applications more efficiently. An introduction to the Wolfram Language symbolic programming, Mathematica, is provided. Mathematica is used to study both empirical trends as well as engineering model simulations based on the engineering extension of game theory. A review of the concepts from physics and mathematics that underlie the approach is given. Along with these introductory foundations, economic applications will be presented and shown to depend on aspects of modern theories of differential geometry.

Though this course begins with classical game theory, it differs from the usual approaches to dynamic games and deals with incomplete information by using constraints in a geometric theory, where the shortest path provides a deterministic prediction of future behaviors. Students will learn to apply introductory ideas to a system without constraints. Next, students will explore the consequences of adding constraints and will be provided an application guide.

CSC6605 Machine Learning Production Systems: Alternate Prerequisites

Although CSC5201 is not a prerequisite for this class, students will benefit from having CSC5201 first whenever possible.

CSC2621 Introduction to Data Science (a course in the DS minor) is a sufficient prerequisite to take this course.

Also, CS and SE students will generally meet the following alternate prerequisites by the time they have early entry status.

Requirement Quarter Semester
(Databases and CS3860 CSC3320
Web Apps) or SE2840 SWE2511
Machine Learning CS3400 CSC4601

CSC7901 Machine Learning Capstone

Enrollment Steps

The steps for enrolling in the capstone project course are:

  1. About a year before your capstone begins:
    1. Receive approval from the capstone coordinator to begin your capstone in a particular semester.
    2. Discuss and refine your project idea with potential capstone advisors.
    3. Submit a paragraph describing your project idea to the capstone coordinator.
    4. Keep the capstone coordinator updated if your project idea changes significantly and on any advisor preferences.
  2. About a semester before your capstone begins:
    1. The capstone coordinator notifies you who your advisor will be. (Beginning with Spring, 2025 projects; in earlier semesters students find their advisors by mutual agreement with the advisor.)
    2. Discuss and further refine your project idea in consultation with your advisor.
    3. Work with your capstone advisor to identify 2 other MSOE faculty members to serve on your committee. Outside committee members (e.g., the chief data scientist at your company) are allowed via adjunct appointments if they have appropriate experience and background, including at a minimum a relevant master’s degree.
    4. Complete the form, get required signatures, and submit it. See the form for the deadline, but submit it sooner if possible. Then the class is added to your schedule for the next semester.
      • Fall 2024 Projects: Use this form and submit it to your project advisor who will send the form on to the next steps in the process.
      • Spring 2025 Projects and Beyond: The form will be posted after August 1 on the Registrar’s website.

Capstone Project Options

Students may work:

Potential Capstone Project Advisors

Please contact these faculty and others to help refine your project idea:

Capstone Project Process

  1. Committee Role: A capstone committee member is available as a technical resource throughout the project and participates in the midterm review and final presentation.
  2. Weekly Meetings: The advisor and student will discuss project planning and intermediate results. The advisor may request a memo or slides to guide weekly discussions.
  3. Initial Meeting: In the first week of the term, the student presents a preliminary project timeline, outlining tasks such as background research, addressing ethical and governance issues, data collection, data preprocessing and exploration, model exploration, implementation, preliminary results, model refinements, evaluation, final paper writing, and presentation preparation. Students may share drafts for input before this meeting.
  4. Midterm Review: Early in the term, the student must contact the committee to schedule a 50–80 minute meeting at least one month prior to the final presentation to present preliminary results in detail and a revised plan for project completion.
  5. Final Presentation: By mid-term, the student must contact the committee to schedule a 50–80 minute final presentation around the Monday of finals week. One week prior, the near-final report is due to the committee. The committee provides feedback before, during, or immediately after the final presentation. The student incorporates this feedback, including any changes requested, and submits an updated final report during finals week. The advisor will determine if further review from the committee is necessary.


For MSOE undergraduates, this class cannot meet the undergraduate ethics requirement, and the undergraduate ethics requirement cannot meet this MSML requirement.

Many undergraduate majors allow a choice of ethics class. Students in these majors, if intending to pursue the MSML, should consider taking PHL3101 Ethics for Professional Managers and Engineers or PHL3103 Bioethics, which have almost no overlap with PHL6001. They may also take PHL3102 Ethics of Digital Technologies and AI, which has a partial overlap with PHL6001.

Course Details

Click on course numbers below to see catalog entries with course details.

Course Type Title Structure Offered Coordinator Prerequisites
BME5210 Elective Medical Imaging Systems 3-2-4   Dr. Imas ELE3300 | CSC4651 | CSC5651 | CSC4611 | CSC5611 | CSC6621
CSC5120 Elective Software Development for Machine Learning 4-0-4 summer [Dr. Magaña][Dr. Magaña] CSC1110 | CSC1310 | consent
CSC5201 Required Microservices and Cloud Computing 4-0-4 fall Dr. Nowling CSC5120 | CSC1120 | CS2852 | equiv. software background | consent |
CSC5241 Elective GPU Programming 4-0-4   Dr. Berisha CSC2210 | consent
CSC5601 Elective Theory of Machine Learning 4-0-4 fall Dr. Bukowy (((MTH2130 & MTH2340) | MTH5810) & (CSC2621 | CSC5610)) | consent
CSC5610 Required AI Tools and Paradigms 4-0-4 fall Dr. Nowling (MTH1120 | MTH2340 | MTH5810) & (CSC1120 | equivalent) | consent
CSC5611 Elective Deep Learning 4-0-4 spring Dr. Yoder CSC4601 | CSC5601 | CSC6621 | consent
CSC5651 Elective Deep Learning in Signal Processing 4-0-4 2025-‘26 Dr. Durant (MTH5810 | MTH2340 | MTH2130) & (CSC5610 | ELE3320 | CSC3310 | CSC2621)
CSC5661 Elective Reinforcement Learning 4-0-4 fall, 2024 then TBD Dr. Kedziora ((MTH2480 & MTH2340) | MTH5810) & (CSC2621 | CSC5610) | consent
CSC5980(1) Elective Topics in Computer Science (with Laboratory) varies      
CSC6605 Required Machine Learning Production Systems 4-0-4 spring Dr. Nowling CSC5610 | CS3400 | CSC4601 | CSC5601 | consent
CSC6621 Required Applied Machine Learning 4-0-4 spring Dr. Nowling (CSC5610 | CSC2621) & (MTH2130 | MTH2340 | MTH5810) | consent | CS2300
CSC6711 Elective Recommendation Systems 4-0-4   Dr. Nowling (CSC5610 | (CSC2611 & CSC3310) | CSC6621) & (MTH2340 | MTH5810) | instructor consent
CSC6712 Elective Distributed Database Systems 4-0-4   Dr. Nowling CSC5201 | instructor consent
CSC6980 Elective Topics in Computer Science varies      
CSC6999 Elective Computer Science Independent Study varies      
CSC7901 Required Machine Learning Capstone 4-0-4 all terms Dr. Kedziora ≤ 1 year to completion
MTH5810 Required Mathematical Methods for Machine Learning 4-0-4 fall Dr. Armstrong Enrolled in MSML
PHL6001 Required AI Ethics and Governance 4-0-4 summer Dr. McAninch Enrolled in MSML

Past and Planned Offerings