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 systems development and deployment. The program provides students with the skills necessary to develop and deploy machine learning solutions in their technical fields. The program is open to students who have a bachelor’s degree in a technical field and significant 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, and faculty who excel in teaching, research, and student support.

MSOE has distinguished itself as a leader in the area of Machine Learning (ML) with the first undergraduate Computer Science program focused on applications of artificial intelligence. The M.S. in Machine Learning supports students who wish to develop the advanced technical skills needed to create new products integrating ML and big data. While there are many post-baccalaureate programs that introduce students to the concepts of ML and touch on areas such as natural language processing and computer vision, few if any programs are geared towards the application of ML to industrial problems and the development and deployment of ML-based products. This 32-credit program provides the depth of content necessary to develop ML-based solutions. 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 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.

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

Faculty

Dr. Sebastian Berisha, Dr. John Bukowy, Dr. Eric Durant, Dr. Jonathon Flynn, Dr. Jeremy Kedziora, Dr. RJ Nowling, Dr. Jay Urbain, Dr. Josiah Yoder