The Milwaukee School of Engineering (MSOE) offers a Master of Science in Machine Learning for students and working professionals who want advanced training in machine learning, artificial intelligence, and the development of production-ready ML systems.
The 32-credit program is delivered 100% online through live, synchronous classes. Students study applied machine learning, cloud computing, production systems, mathematics for machine learning, AI ethics, and related technical subjects.
Quick Facts
| Degree | Master of Science in Machine Learning |
| School | Milwaukee School of Engineering |
| Location | Milwaukee, Wisconsin |
| Format | Online, synchronous |
| Credits | 32 |
| Typical Length | 2 years full-time; 3 years part-time |
| Tuition | $1,718 per credit for 2026–27 |
| Estimated Base Tuition | $54,976 |
| Minimum GPA | 3.0 |
| GRE | Not listed as required |
Program Overview
MSOE’s M.S. in Machine Learning focuses on the practical development and deployment of machine learning systems.
The program is built for students who already have a technical background. Rather than starting with introductory programming and mathematics, the curriculum moves into applied machine learning, production systems, cloud computing, and advanced AI topics.
Classes are online but are not self-paced. MSOE uses synchronous instruction, with live two-hour classes held two nights per week.
Students also receive access to Rosie, MSOE’s computing cluster. Rosie includes NVIDIA GPU hardware and is available to graduate students for machine learning and other computational work.
Visit the official MSOE M.S. in Machine Learning program page.
Curriculum
The M.S. in Machine Learning requires 32 credits, including 28 credits of required coursework and one 4-credit elective.
Required Courses
| Course | Credits |
|---|---|
| CSC 5201 – Microservices and Cloud Computing | 4 |
| CSC 5610 – AI Tools and Paradigms | 4 |
| CSC 6605 – Machine Learning Production Systems | 4 |
| CSC 6621 – Applied Machine Learning | 4 |
| CSC 7901 – Machine Learning Capstone | 4 |
| MTH 5810 – Mathematical Methods for Machine Learning | 4 |
| PHL 6001 – AI Ethics and Governance | 4 |
The curriculum covers more than model development. Students learn how to build, deploy, evaluate, and manage machine learning systems in real applications.
The Machine Learning Capstone serves as the culminating course.
Electives
Students complete one 4-credit elective. Current options include subjects such as:
- Deep Learning
- Reinforcement Learning
- LLMs in Production
- Recommendation Systems
- Distributed Storage Systems
- ML on Embedded Systems
- Theory of Machine Learning
- Software Development for Machine Learning
- Analytics Leadership and Strategy
Elective availability and approved substitutions can change, so students should confirm current options with MSOE.
View the official MSOE machine learning curriculum.
Specialization and Certificate Options
One unusual feature of the MSOE program is its use of stackable graduate certificates.
Students can use their coursework to develop a concentration in areas such as:
- Applied Machine Learning
- Machine Learning Engineering
- Tiny Machine Learning
- Generative AI Production Systems
Each certificate consists of two 4-credit courses. Students may begin with a certificate or earn qualifying certificates while completing the master’s degree without adding credits when the coursework overlaps.
This structure can appeal to students who want a shorter graduate credential before committing to the full master’s program.
Online Format and Program Length
MSOE offers the M.S. in Machine Learning 100% online, but students should understand that the program uses scheduled live classes rather than an asynchronous format.
MSOE lists two main completion paths:
Full-time: Students take two courses, or 8 credits, per semester and can complete the degree in about two years.
Part-time: Students take one 4-credit course per semester and can complete the program in about three years.
The scheduled evening format is aimed in part at working professionals who want live interaction with faculty and classmates.
Admissions Requirements
Applicants should have a technical bachelor’s degree and a minimum undergraduate GPA of 3.0 on a 4.0 scale. MSOE states that applicants below the 3.0 threshold may receive case-by-case consideration.
Applicants also need:
- At least one year of college-level programming or equivalent experience
- Experience with an object-oriented programming language such as Python or C++
- At least one year of differential and integral calculus
A data structures course is preferred. Multivariable calculus or linear algebra is also preferred. Students who lack some of these courses may be able to complete them at MSOE.
The required application documents include an official undergraduate transcript and resume.
Tuition and Fees
MSOE lists tuition for the M.S. in Machine Learning at $1,718 per credit for the 2026–27 academic year.
For a 32-credit degree, that produces estimated base tuition of:
32 credits × $1,718 = $54,976
Graduate students also pay a $35-per-credit technology fee, which would add approximately $1,120 across 32 credits at the current rate.
This puts estimated tuition plus the technology fee at approximately $56,096, before books or other expenses.
MSOE notes that scholarships are available for M.S. in Machine Learning students. Tuition and fees can change, so prospective students should verify current costs directly with the university.
Check current MSOE graduate tuition and fees.
Who Is This Program Best For?
MSOE’s master’s program may be a strong option for students who want a technical, applied machine learning degree delivered online.
It is especially worth considering if you want to:
- Build and deploy machine learning systems rather than focus only on theory
- Study while continuing to work
- Attend live online classes with faculty and classmates
- Develop skills in ML engineering and production systems
- Study areas such as deep learning, reinforcement learning, LLM production systems, or embedded machine learning
- Earn a stackable machine learning certificate as part of the master’s degree
Students who need a fully asynchronous program may want to compare other options. MSOE requires participation in scheduled synchronous online classes.
Key Takeaway
The Milwaukee School of Engineering M.S. in Machine Learning stands out for its focus on applied machine learning and the engineering work required to put ML systems into production.
The program requires 32 credits and combines machine learning with cloud computing, production systems, mathematics, ethics, and a capstone. Students can also use electives to develop skills in areas such as deep learning, reinforcement learning, LLMs, recommendation systems, and embedded ML.
The program is fully online, but its live evening classes make it different from self-paced online master’s programs.
For students who already have programming and calculus experience and want a practical path into machine learning engineering, MSOE is a program worth comparing with other technical ML master’s degrees.
Official Program Links
- MSOE M.S. in Machine Learning
- MSOE Online M.S. in Machine Learning details
- MSOE Graduate Tuition and Fees