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 program co-advisors via email:
- Dr. Sennur Ulukus: ulukus@umd.edu
- Dr. David Jacobs: djacobs@cs.umd.edu
Overview
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.
Courses
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 |
Overall
- 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.