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 Fall 2026, we will have two new courses which are not yet listed in the catalog:

CSC 6608: ML on Embedded Systems The class will focus on the use of machine learning models on low-powered embedded devices. Students will learn about the hardware and software characteristics of an embedded system platform including computational, memory, and power constraints. Students will learn to use a development environment, libraries, and appropriate algorithms to write software for an embedded system. Libraries will be used to read streaming sensor data from the device and control output devices. Students will train and evaluate ML models for various tasks that use sensor data as input. Tradeoffs in prediction performance, computational and memory use, and power consumption with regards to different ML model types, hyper-parameters set at training time, and post-processing techniques for reducing model sizes will be demonstrated. Students will learn to integrate feature engineering logic and trained ML models into software deployed on the device. Both classical ML and deep learning techniques will be covered. Aspects such as product requirements, hardware capabilities, reliability, and on-device testing/validation will be emphasized throughout.

Prereq: CSC 5616 or instructor consent

CSC 6617: Large Language Models This course introduces large language models (LLMs) from the ground up. Beginning with a discussion of the implementation of a modern LLM and its training, the course includes discussion of the applications of LLMs and a mechanism-aware discussion of prompt engineering. By the end of the course, students will have a concrete understanding of how LLMs perform computations and be able to apply that knowledge to use LLMs to solve real-world tasks.

Prereq: (MTH 2340, MTH 5810, or similar course work) and (CSC 2621, CSC 5610, or similar coursework) or instructor consent

Production Systems Restricted Elective

With the launch of the two new course sequences, we have made the MSML requirements more flexible. Students may take CSC 6607 LLMs in Production or CSC 6608 ML on Embedded Systems in place of CSC 6605 ML Production Systems.

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 approval is determined based on the feasibility of, defined scope of, clarify of the description for, and 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.

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.

Probation and Suspension

• A student whose graduate cumulative GPA is less than 3.00 or the student receives a grade of F in any graduate class during the previous semester is placed on probation starting the next semester.
• To return to good academic standing, the student's cumulative GPA must be 3.00 or higher and they must not have received any F grades in the previous semester.
• A student who is on probation will be suspended if:
  • they receive a grade of F in any class
  • OR their graduate term GPA is below 2.00 during any semester in which they are on probation
• Suspensions generally last two semesters. After the suspension time has passed, students are required to petition the Graduate Student Advancement Subcommittee to continue their studies.
• Students in the early entry program are subject to the undergraduate policies for probation and suspension. Early entry students who are placed on suspension during their B.S. are not eligible to continue in the early entry program.

Graduate Certificates

The Diercks School of Advanced Computing also offers four certificates:

• Applied ML: Develop a foundation in data science and creating machine and deep learning models.
  • CSC 5610 AI Tools and Paradigms
  • CSC 6621 Applied ML
• Generative AI Production Systems: Learn how to create and deploy AI-powered, interactive software systems.
  • CSC 6714 Large Language Models
  • CSC 6607 LLMs in Production
• ML Engineering: Learn how to deploy and manage machine and deep learning models as software services.
  • CSC 5201 Microservices and Cloud Computing (CSC 6711 or CSC 6712 may be substituted at program director's discretion)
  • CSC 6605 ML Production Systems
• Tiny Machine Learning (TinyML): Create models for signal data and deploy them on embedded systems (microcontrollers)
  • CSC 5616 ML for Signal Processing Applications
  • CSC 6608 ML on Embedded Systems

Each certificate requires completing 2 courses. These course overlap completely with the MSML, so a student can earn some of the certificates and MSML. This gives students the option of starting in a certificate and migrating to the MSML or using certificates as indicators of specializations.

Declaring a Certificate

Existing MSML students can declare a certificate using the form for current students on the Registrar's Office web page.

Existing certificate students who wish to pursue an additional certificate should reach out to Dr. Nowling to request that they be considered for admission.

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.