Machine Learning, M.S.

Program Director

RJ Nowling, Ph.D.
Office: DH-421
Phone: (414) 277-7318
Email: nowling@msoe.edu

Overview

The Master of Science in Machine Learning program is an online program geared towards students who wish to develop advanced skills in machine learning (ML) and artificial intelligence (AI). The program is geared towards student with technical and engineering backgrounds and aims to provide unprecedented depth to its students. It leverages the student’s existing skills in programming and application area knowledge to dive right into advanced concepts that can be applied immediately. The program covers both the development of models as well as integration, deployment, and management of models in production software systems. Through elective choices, students can develop specializations and earn graduate certificates in Generative AI Production Systems, ML Engineering, and Tiny Machine Learning.

The program accepts students who have a bachelor’s degree in a technical field and previous programming experience. Key features of this program include depth of technical content, industry applications and integration in every course, access to ROSIE (MSOE’s GPU cluster), small class sizes, faculty who excel in teaching, research, and student support, and online, synchronous evening courses.

The program is open to both full-time and part-time students. The eight required courses can be taken at a pace of one or two courses per semester, including summer. Five-year BS/MS paths through several MSOE undergraduate programs provide another option for completion.

Faculty

Dr. Sebastian Berisha, Dr. John Bukowy, Dr. Jonathon Flynn, Dr. Jeremy Kedziora, Dr. Richard Lukas, Jr., Dr. RJ Nowling, Dr. William Retert, Dr. Jay Urbain, Dr. Josiah Yoder

Program Educational Objectives

The Machine Learning Master’s program will prepare graduates, within a few years of graduation, to:

Be the lead architect on complex projects involving machine learning and data science
Develop solutions that address competing ethical and professional concerns as both technology and society continue to evolve
Pursue continued technical and professional development

Student Outcomes

Analyze complex problems involving advanced applications of machine learning and data science and design solutions that meet relevant business, technical, and ethical standards
Apply a rigorous, scientific approach that includes forming research questions, generating hypotheses, designing and executing experiments, and evaluating results to make informed judgements
Effectively evaluate and utilize state-of-the-art software and parallel computing hardware in the design and implementation of projects
Effectively describe solutions and their implications and communicate results to technical and non-technical audiences
Successfully deploy production-quality solutions involving machine learning and data science techniques using current best practices