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Advising & Curriculum Overview

Mentoring and advising are an essential part of the program. Students meet with faculty and the academic program director to ensure that educational goals and career learning and development goals are met. To learn more, prospective students should contact the program co-advisors via email:


  • A 30-credit, 10-course, non-thesis graduate program designed for students to acquire the skills and knowledge necessary for a career in today’s information-based society. Successful graduates will apply the learned tools and techniques to a wide variety of real world problems in areas such as marketing, finance, medicine, telecommunications, biology, security, engineering, social networking, and information technology.
  • The degree requirements consists of successful completion of 6 core courses and 4 elective courses. Additional program policies are as follows:
    • Students admitted to the MPS in Machine Learning with appropriate backgrounds can place out of MSML603 and/or MSML604 and take electives instead. The students’ background will be assessed via a placement exam taken at the beginning of their first semester in the program.
    • Students will be able to take one non-MSML course and count it toward their MPS degree if the course content is relevant and its level is appropriate to the MPS in Machine Learning. The non-MSML course will need to be approved by the program adviser.
    • Students will be able to transfer up to 6 credits into the MPS in Machine Learning from other graduate programs if the course’s content is relevant to the MPS in Machine Learning and its level is appropriate. The credit transfer will require the program adviser’s approval.
    • Students admitted to other programs will not be able transfer to the MPS in Machine Learning. All students must specifically apply to the MPS in Machine Learning and gain admission.
  • The program offers students the opportunity to engage in cutting-edge technical coursework in machine learning and develop their problem-solving skills in the art and science of processing and extracting information from data with special emphasis on large amounts of data (Big Data). During their coursework, students will build solid foundations in mathematics, statistics, and computer programming, and explore advanced topics in machine learning such as deep learning, optimization, big data analysis, and signal/image understanding.

 In-Person Learning

  • Instruction provided by University of Maryland faculty and professionals in the field.
  • Instructors present dynamic and interactive seminar-style instruction.
  • Classes meet in UMD College Park campus classrooms, offering a focused, distraction-free learning environment.
  • Classes held weekday evenings (e.g., after 5:00 p.m.) to accommodate the working professional’s schedule.
  • Students enrolled in a program that features in-person instruction are required to submit the University’s Immunization Record Form prior to the first day of their first semester/term. See Health Requirements.
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