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Master of Science in Machine Learning

The Science Academy, housed in the College of Computer, Mathematical, and Natural Sciences (CMNS), draws on the university’s collective expertise to provide academic programs that are both rigorous and relevant. Science Academy Graduate Programs translate research into applied knowledge and provides current and future professionals with invaluable skills.

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. Students should contact the contact the Science Academy staff via email:


The Master of Science in Machine Learning is 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.

  • Program focuses on the methods and techniques of creating models and algorithms that learn from, and make decisions or predictions, based on data. And students complete 6 core courses and 4 elective courses.
  • Successful graduates 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.
  • Students 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).
  • Students 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.
  • Can be completed in sixteen months of continuous full-time enrollment. See Designation of Full-time/Part-time Status.

Program Policies

  • Students admitted to the MS 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 degree if the course content is relevant and its level is appropriate to the MS 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 MS in Machine Learning from other graduate programs if the course’s content is relevant to the MS 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 MS in Machine Learning. All students must specifically apply to the MS in Machine Learning and gain admission.


Below is a listing of all program courses. For a detailed course description that includes pre-requisites or co-requisites, see The Graduate School Catalog, Course Listing as follows: MSML Course Descriptions

Type Course Number Title
Core MSML601 Probability and Statistics
Core MSML602 Principles of Data Science
Core MSML603 Principles of Machine Learning
Core MSML604 Introduction to Optimization
Core MSML605 Computing Systems for Machine Learning
Core MSML606 Algorithms and Data Structures for Machine Learning
Elective MSML610 Advanced Machine Learning
Elective MSML612 Deep Learning
Elective MSML640 Computer Vision
Elective MSML641 Natural Language Processing
Elective MSML642 Robotics
Elective MSML650 Cloud Computing
Elective MSML651 Big Data Analytics

Registration Overview

  • See the sample plan of study, below. Students should use this as a guide to develop a plan with the academic program director. 
  • Actual course offerings are determined by the program and may vary semester to semester. Students should note if a course has a pre-requisite or co-requisite. 
  • Specific class meeting information (days and time) is posted on UMD’s interactive web service services, Testudo. Once on that site, select “Schedule of Classes,” then the term/year. Courses are listed by academic unit. 
  • The program uses specific section codes for registration, which are listed on the sample plan of study.

Sample Plan, Full-Time

Semester Year Course Number Section Code Credits
Fall 1 MSML601 PCS* 3
Fall 1 MSML602 PCS* 3
Fall 1 MSML603 PCS* 3
Spring 1 MSML604 PCS* 3
Spring 1 MSML605 PCS* 3
Spring 1 MSML641 PCS* 3
Summer 1 MSML612 PCS* 3
Summer 2 MSML606 PCS* 3
Fall 2 MSML642 PCS* 3
Fall 2 MSML650 PCS* 3

Sample Plan, Part-Time

Semester Year Course Number Section Code Credits
Fall 1 MSML601 PCS* 3
Fall 1 MSML603 PCS* 3
Spring 1 MSML604 PCS* 3
Spring 1 MSML605 PCS* 3
Summer 1 MSML606 PCS* 3
Summer 1 MSML612 PCS* 3
Fall 2 MSML602 PCS* 3
Fall 2 MSML650 PCS* 3
Spring 2 MSML641 PCS* 3
Spring 2 MSML651 PCS* 3


  • Uses the semester academic calendar with classes held in the fall and spring semester (16 weeks each).
  • Instructors present dynamic and interactive seminar-style instruction. 
  • Instruction provided by University of Maryland faculty and professionals in the field.

In-Person Learning 

  • Classes meet in UMD College Park campus classrooms, offering a focused, distraction-free learning environment. 
  • Classes are mostly 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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