• Overview
• Dual Enrollment with Bachelor’s Degree
  • General 4+1 / Early Entry Policies Including Tuition
  • 8 MB PowerPoint Overview Presentation on MSOE ML Programs
• Official Websites
  • Catalog
  • Academics
  • Admissions
  • Landing / Information Request
• Accreditation
• Machine Learning Careers In The News
• Direct Admit Requirements (also apply to Graduate Certificate in Applied Machine Learning)
• Curriculum
  • Required Courses
  • Example Plan
  • Advising
  • 4+1 Model Tracks—Early Entry
• Notes on Specific Courses
  • Quarter System Applied ML Graduate Certificate (2021–’22 and 2022–’23)
  • CSC5201 Microservices & Cloud Computing
    • Substitutions Allowed
    • Alternate Prerequisites
      • Acceptable Web Apps Coursework
      • Acceptable Cloud Computing Coursework
  • CSC5601 Theory of Machine Learning: CS Major and MSML Elective
  • CSC6605 Machine Learning Production Systems: Alternate Prerequisites
  • CSC7901 Machine Learning Capstone
  • PHL6001
• Course Details
• Planned Offerings and Faculty Assignments

Overview

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.

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. Students must apply while they have junior standing, but should let their advisor know of their intent as early as possible.

• One or more summer courses are usually required.
• BMEs entering in F23 or later should choose the “AI Focus” option.
• All EECS undergraduate majors allow their free and program elective requirements to be met by MSML courses. Check with your advisor if not in EECS.
  • MSOE allows a maximum of 16 credits of graduate courses (i.e., 4 MSML courses) to be taken before the BS degree is confered.
  • MSML courses meeting an undergraduate requirement count toward full-time undergraduate enrollment.
  • Normal undergraduate tuition applies prior to completing the undergraduate degree requirements.

General 4+1 / Early Entry Policies Including Tuition

8 MB PowerPoint Overview Presentation on MSOE ML Programs

Official Websites

Catalog

• https://catalog.msoe.edu/. Select 2023-2024 Graduate Catalog.

Academics

• MS in Machine Learning
• Graduate Certificate in Machine Learning
• Graduate Certificate in Machine Learning Engineering - offered in fall, 2023; site coming soon

Admissions

• Machine Learning Programs
• MS in Machine Learning
• Graduate Certificate in Machine Learning
• Graduate Certificate in Machine Learning Engineering - offered in fall, 2023; site coming soon

Landing / Information Request

• MS in Machine Learning
• Graduate Certificate in Machine Learning
• Graduate Certificate in Machine Learning Engineering - offered in fall, 2023; site coming soon

Accreditation

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

• 2023-07-28 MSOE post on ML Careers and Industry Growth
• 2022-08-19 MSOE Post for Employers on Developing ML Talent
• 2023-08-14 Wall Street Journal article on demand for AI experts

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

• Technical bachelor’s degree
• Programming experience with a modern language such as Python, C#, or Java. One year of college coursework (culminating in e.g., CS2852, CSC1120, or CSC2611 or equivalent minimum.
  • Students with less experience (similar to CS1021, CSC1110, or CSC1310) will be encouraged to take CSC5120 Software Development for Machine Learning over the summer. This can count as an elective in the Master of Science in Machine Learning.
• At least a year of differential and integral calculus required. Multivariable calculus or linear algebra preferred.
  • Students who don’t have both topics will take MTH5810 Mathematical Methods for Machine Learning; students who do will take an additional elective.

Curriculum

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.

• Subset 1: Applied Machine Learning Graduate Certificate
  • CSC5610 AI Tools and Paradigms
  • CSC6621 Applied ML (prerequisite of CSC5610)
• Subset 2: Machine Learning Engineering Graduate Certificate
  • CSC6605 ML Production Systems (prerequisite of CSC5610)
  • CSC5201 Microservices & Cloud Computing
• MTH5810 Math Methods for ML
• PHL6001 AI Ethics and Governance
• CSC5xxx Elective (can be met with CSC5120 if this course is needed)
• CSC7901 Capstone

MSOE CS students and graduates replace CSC5610 and MTH5810 with approved electives.

Example Plan

Advising

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.

4+1 Model Tracks—Early Entry

Model tracks are published for BME, CE, CS, EE, and SE.

Notes on Specific Courses

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

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

CSC5201 Microservices & Cloud Computing

Substitutions Allowed

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.

Alternate Prerequisites

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

Acceptable Web Apps Coursework

• SE2840 Web Application Development, Required for CS and SE
• SWE2511 Web Apps, Required for SWE

Acceptable Cloud Computing Coursework

• CS4230 / CSC4981 Distributed and Cloud Computing, Elective

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.

CSC6605 Machine Learning Production Systems: Alternate Prerequisites

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.

CSC7901 Machine Learning Capstone

The steps for enrolling in the capstone project course are:

1. Get approval from the program director to begin your capstone in a particular semester.
2. Find a capstone advisor and discuss and refine your project idea.
3. Work with your capstone advisor to identify 2 other MSOE faculty members to serve on your committee.
4. Complete the form posted on the Registrar’s website, get required signatures, and submit it. Then the class is added to your schedule for the next semester.

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.

• Dr. Berisha: Machine/deep learning, image processing, time-series data analysis. In general, my research centers around the application of machine learning algorithms in the fields of biomedicine and remote sensing. My most recent efforts have concentrated on two significant applications. Firstly, I have been exploring the integration of deep learning with hyperspectral mid-infrared imaging to enhance cancer diagnosis. Secondly, my research is dedicated to the application of deep learning techniques in the analysis of ECG data for the diagnosis of cardiovascular, brain injury, and respiratory diseases.
• Dr. Durant: Signal processing, real-time systems, audio
• Dr. Kedziora: Data science, statistics, machine learning, reinforcement learning
• Dr. Nowling: Data science, ML and data-intensive systems (production, recommendation, distributed, etc.)
• Dr. Urbain: Natural language processing, medical image processing, graph machine learning

PHL6001

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.

• Students may not receive credit for CSC5610 if they have completed CSC2611 AI Tools and CSC2621 Intro to Data Science. Instead, they take an MSML elective.
• Offering information is fairly firm but might change.

Planned Offerings and Faculty Assignments

• Summer, 2023
  • CSC5120: Dr. Magaña
• Fall, 2023
  • CSC5610: Dr. Nowling and Dr. Bukowy
  • CSC5651: Dr. Durant
  • CSC6605: Dr. Nowling
• Spring, 2024
  • CSC5201: Dr. Urbain
  • CSC5601: Dr. Durant (in person)
  • CSC5610: Dr. Nowling (off-term offering)
  • CSC6621: Dr. Berisha (2 sections)
  • CSC6711: Dr. Nowling
• Summer, 2024
  • CSC5120: Dr. Magaña
  • CSC6621: Dr. Berisha (off-term offering)
  • CSC6712: Dr. Nowling
  • PHL6001: Dr. McAninch (7-week subterm 1)
• Fall, 2024
  • CSC5201: Dr. Urbain
  • CSC5601: Dr. Wang (in person)
  • CSC5610: Dr. Nowling (2 sections)
  • CSC5611: Dr. Yoder (in person)
  • CSC5661: Dr. Kedziora
  • MTH5810: Dr. Armstrong
  • …
• Spring, 2025
  • CSC5611: TBD
  • CSC6605: TBD
  • CSC6621: TBD
  • …