The M.S. in Machine Learning 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. The program includes depth of technical content, industry applications in every course, use of Rosie the supercomputer, small class sizes and faculty who excel in teaching, research and student support. Students gain the skills they need to develop and deploy machine learning solutions in their technical fields. The M.S. in Machine Learning program prepares graduates to be lead architects on complex projects involving machine learning and data science, develop solutions that address ethical and professional concerns as both technology and society continue to evolve, and pursue continued technical and professional development.
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.
| Mode of Delivery | Program Credits | Admissions Requirements |
| Online synchronous (two nights a week, 2-hour live lecture) | 32 credits |
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If you’re looking for a program that leverages cutting-edge A.I. and analytics without the heavy math prerequisites, consider the Graduate Certificate in Advanced Business Strategy Using AI and Analytics offered by the Rader School of Business. This program is designed to equip you with the strategic insights and analytical skills needed to excel in today’s data-driven business environment.
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.
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.