Master's in Machine Learning

Program Requirements

For current program requirements, please see the M.S. in Machine Learning program page in the Graduate Course Catalog.

Planned Course Offerings

Course descriptions can be found in the Graduate Course Catalog.

Special topics courses may be offered occasionally under the CSC 5980 course code. Course descriptions will be posted here. In Spring 2026, we will offer:

CSC 5980: ML for Signal Processing Applications This course provides an applied survey of computational tasks that can be performed with signal data using associated methods drawn from the fields of digital signal processing (DSP) and machine learning (ML). DSP concepts such as time versus frequency domain as well as related methods such as convolutional filters and spectral analysis will be introduced through an applied, illustrative examples. DSP concepts will be used to motivate feature engineering for applying classical machine learning to signal data and usage of deep learning architectures such convolutional neural networks (CNNs). Students will gain hands-on experience with a variety of data sets and problems drawn from a range of application domains. Emphasis will be placed on low-dimensional, time-series data frequently encountered in embedded systems contexts.

Prereq: (MTH 5810 or MTH 2340 or MTH 2130) and (CSC 5610 or CSC 4601 or CSC 5601) or consent of instructor

New Course Sequences

We will be rolling out two new course sequences over the next few semesters. These students will allow students to focus their MSML degree on one of two emerging paradigms:

TinyML

Machine learning isn't just used on high-end clusters and cloud computing services; increasingly, ML models are deployed to small, low-power embedded systems. We will offer a 2-course sequence in TinyML, an area the focuses on ML on embedded systems.

• CSC 5616 ML for Signal Processing Applications (offered Spring 2026 as a CSC 5980 special topics course)
• CSC 6608 ML on Embedded Systems (offered starting Fall 2026): provides an introduction to programming embedded systems, including tasks such as reading data from sensors, as well as libraries and approaches for shrinking and incorporating ML models into software running on embedded devices. Students will gain hands-on experience with an embedded platform.

ML for Signal Processing Applications is a prerequisite for CSC 6608, so students interested in taking CSC 6608 in Fall 2026 should plan to take CSC 5980 in Spring 2026.

Generative AI Production Systems

LLMs are seeing rapid adoption as components in software systems for data transformation, reasoning, and synthesis tasks. We will offer a 2-course sequence in Generative AI Production Systems that covers the internals and use of LLMs as well as implementing and deploying complex workflows using LLMs.

• CSC 6714 Large Language Models (first offered Fall 2026)
• CSC 6607 LLMs in Production (first offering TBD)

CSC 6607 requires CSC 6714 or CSC 5611 Deep Learning as a prerequisite.

Early Entry Program

MSOE undergraduate students can apply for our B.S./M.S. early entry program. Up to 16 credits of graduate classes can be taken before completing the B.S. and double counted towards both degrees. Students can continue full-time at MSOE to finish the M.S. courses or switch to part-time, taking one course at a time. For most students, the remaining M.S. requirements can be completed in two semesters, one of which can be a Summer semester.

When to Apply

Students can apply once they've completed 60 credits (have junior standing). For many students, however, it is advantageous to begin discussing the early entry program with your academic advisor as soon as possible. There are often courses taken during the junior year that can be moved around or specific choices for electives that can make it easier to complete the combined B.S/M.S. program.

How to Apply

Current MSOE students can fill out the Early Entry Application form.

Planning for the Early Entry Program

Some specific advice that can be helpful:

• Undergraduate students must have senior standing (90 credits) and be within one year of completing their B.S. to take graduate courses. For many students, they take their first graduate course the summer before starting their senior year.
• CS majors often push CSC 4601 Theory of ML and CSC 4611 Deep Learning from the junior year to the senior year so that they can take the graduate versions (CSC 5601 and CSC 5611).
• Students who take PHL 3102 Ethics of Digital Technologies and Artificial Intelligence can replace the required PHL 6001 AI Ethics and Governance course with an elective. It is recommended that students whose programs offer a choice of ethics electives chose PHL 3102.
• Completion of the MSML requires both MTH 2130 Calculus III and MTH 2340 Linear Algebra with Applications. These should be taken before the completion of the B.S.
• It is strongly recommended that students take CSC 5120 Microservices and Cloud Computing before CSC 6605 ML Production Systems.

Academic Advising

In some cases, students will be assigned a new academic advisor if accepted into the Early Entry program. The following faculty are Early Entry advisors for specific programs:

• CS: Dr. Berisha and Dr. Bukowy
• SE: Dr. Taylor

For other majors, students will retain their current academic advisor and Dr. Nowling will work with the academic advisor to develop a plan.

Course Registration Procedure

Early Entry students cannot register themselves for graduate courses until their BS is conferred. We have developed the following process for registering students:

1. The program director (Dr. Nowling) will email students and their advisors at the beginning of the Fall and Spring terms to confirm intended courses and list alternative options.
2. The student will meet with their academic advisor (if necessary).
3. The student will send any changes to Dr. Nowling (if necessary).
4. Dr. Nowling will send a list of students and courses to the Registrar's Office once registration opens and Early Entry students will be placed in their courses.

We plan course capacities one to two semesters ahead based on the expected enrollment from the STAT plans generated by advisors. If a student wants to make changes the semester before, courses may be full, and we may not be able to accommodate them. It is important that students signal their interest in courses by keeping their STAT plans up-to-date.

Note to advisors: Aftering updating a STAT plan with a student, please send it to Dr. Nowling so that I can update a spreadsheet we use to track all course intentions for planning purposes.

International Early Entry Students

The MS in Machine Learning Early Entry program is now able to support international students. Students can only count one online course towards the credits that bring them to full-time status (12 credits for undergraduate students, 9 credits for graduate students) during the Fall and Spring semesters. Additional online courses can be taken if they are beyond the full-time status. This restriction is not in place during summers. International students should reach out to the MSML program director, Dr. Nowling, to discuss options.

Capstone Option

MSML students can take CSC 7901 Machine Learning Capstone as an elective. CSC 7901 provides students with an opportunity to pursue a self-directed project under the guidance of a faculty member. Capstones are useful for students who want to study a topic in greater depth than available in a standard course or gain research experience for pursuing a Ph.D.

Students must have a project approved by and receive permission to register for CSC 7901 from the coordinator Dr. Jeremy Kedziora. Capstone participation is dependent upon the availability of faculty with expertise to advise the proposed project. Students should begin discussing their interest in CSC 7901 with Dr. Kedziora at least one full semester before intending to take it.

CSC 7901 is no longer a requirement of the MSML program. Most students will complete their electives with standard courses.

Certificates

The Diercks School of Advanced Computing also offers two certificates:

• Applied ML
• ML Engineering

Each certificate requires completing 2 courses. These course overlap completely with the MSML, so a student can earn both the certificates and MSML. This gives students options to start in certificates and migrate into the MSML, if desired. The certificates offer two start terms per year (Fall and Spring). The course schedules and requirements are listed below:

Getting Help

Please reach out to Dr. RJ Nowling, the program director, or the Lucia Kohne, the Director of Graduate Admissions, via email at nowling@msoe.edu or kohne@msoe.edu if you have any questions.