M.S. in Machine Learning
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Program Overview
M.S. in Machine Learning
Overview
The Master of Science in Machine Learning is an online program geared toward working professionals who wish to develop advanced skills in the area of machine learning. The program empowers working professionals to leverage their existing skills in programming and application area knowledge, enabling them to dive right into advanced concepts that can be applied immediately.
Program Details
- Mode of Delivery: Online synchronous (two nights a week, 2-hour live lecture)
- Program Credits: 32 credits
- Admissions Requirements:
- Official undergraduate transcripts
- Resume
- Technical bachelor’s degree
- Programming experience with an object-oriented programming language (C++, python, etc.)
- One year of differential and integral calculus (multivariable or linear algebra preferred)
Curriculum Format
Most classes are delivered in a synchronous online format. Faculty are available for both online and in-person meetings. The program is designed for individuals who hold technical bachelor’s degrees, have experience with object-oriented programming, and have taken undergraduate coursework in probability, statistics, and integral calculus. The program is comprised of eight 4-credit courses, totaling 32 credits. Students can complete the eight required courses at a pace of one or two courses per semester, including summer semester.
Stackable Certificates
The M.S. in Machine Learning is organized around two “stackable” certificates: the Applied Machine Learning Graduate Certificate and the Machine Learning Engineering Graduate Certificate. Each certificate is comprised of two 4-credit courses. Students may start out with the certificates or earn them along the way. Students will not need to take additional credits to earn overlapping certificates and degrees.
Admissions Process
- Submit application online.
- Submit supporting documents to admissions counselor via email or mail.
- Completed file reviewed by program director for admission decision.
- Upon acceptance, submit your enrollment deposit to secure your spot in the program.
- Get access to your myMSOE account and MSOE email.
- Admissions Office registers you for appropriate coursework and sends schedule when finalized by the Registrar’s Office.
- You are officially an MSOE Raider!
Tuition & Financial Aid
- Graduate Tuition
- Graduate Financial Aid
Program Outcomes
Graduates of the program will be able to analyze complex problems involving advanced applications of machine learning and data science, and effectively evaluate and utilize state-of-the-art software and parallel computing hardware in the design and implementation of projects. They will be able to successfully deploy production-quality solutions involving machine learning and data science techniques using current best practices.
Research and Facilities
The program utilizes MSOE’s supercomputer, Rosie, which includes three NVIDIA DGX-1 pods, each with eight NVIDIA V100 Tensor Core GPUs, and 20 servers each with four NVIDIA T4 GPUs. The nodes are joined together by Mellanox networking fabric and share 200TB of network-attached storage. The supercomputer is available to both undergraduate and graduate students, offering them the ability to apply their learning in a hands-on environment to prepare for their careers.