This is a new degree offering beginning in the fall of 2023, coinciding with MSOE’s transition to a semester system. It consists of 8, 4-credit courses.
All courses will be offered 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.
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. Students must apply while they have junior standing, but should let their advisor know of their intent as early as possible.
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.
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.
MSOE CS students and graduates replace CSC5610 and MTH5810 with approved electives.
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 semseter.
Model tracks are published for BME, CE, CS, EE, and SE.
Completing this certificate at MSOE meets the CSC5610 and CSC6621 requirements by substitution, reducing the number of additional courses needed to earn the MSML.
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.
Students with sufficient coursework in both web apps and cloud computing replace this course with an approved elective.
Note: SWE2410 Design and Cloud Patterns is not sufficient to meet this requirement.
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.
CSC2621 Introduction to Data Science (a course in the DS minor) is a sufficient prerequisite to take this course.
Generally, CSC and SWE students will meet these requirements by the time they have early entry status.
|Web Apps) or||SE2840||SWE2511|
The steps for enrolling in the capstone project course are:
The following faculty are some of the potential capstone project advisors. Feel free to contact them to see if your areas of interest align with their expertise.
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.
Click on course numbers below to see catalog entries with course details.
|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 beginning ‘23||Dr. Magaña||CSC1110 | CSC1310 | consent|
|CSC5201||Required||Microservices and Cloud Computing||4-0-4||spring ‘24, then fall||Dr. Nowling||CSC5120 | SWE2710 | (CSC3320 & CSC3210) | CPE2600 | consent | …|
|CSC5241||Elective||GPU Programming||4-0-4||Dr. Berisha|
|CSC5601||Elective||Theory of Machine Learning||4-0-4||spring, 2024 then every 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||F24 and spring beginning 2025||Dr. Yoder||CSC4601 | CSC5601 | CSC6621 | consent|
|CSC5651||Elective||Deep Learning in Signal Processing||4-0-4||fall, 2023 then again in 2025-‘26||Dr. Durant|
|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||fall ‘23, springs starting ‘25||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||CSC6605 & (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. Durant||≤ 1 year to completion|
|MTH5810||Required||Mathematical Methods for Machine Learning||4-0-4||fall beginning ‘24||Dr. Armstrong||Enrolled in MSML|
|PHL6001||Required||AI Ethics and Governance||4-0-4||summers beginning ‘24||Dr. McAninch||Enrolled in MSML|