Skip to main content

Plan of Study: MPS - Machine Learning


Plan of Study Overview

The MPS-Machine Learning is a 30-credit graduate program designed to be completed in sixteen months of continuous full-time enrollment. Part-time enrollment is welcome. See Designation of Full-time/Part-time Status.

See the sample plan of study, below. Students should use this as a guide to develop a plan with the program director. The program uses the semester academic calendar with classes held in fall and spring semester (16 weeks each) and Summer Session (two 6-week sessions).  

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.  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. UMD graduate students in other programs who are interested in registering for courses in this program, see Non-Program Student Registration.

Semester Year Type Course Number Section Code Credits
Fall 1 Core MSML601 PCS* 3
Fall 1 Core MSML602 PCS* 3
Fall 1 Core MSML603 PCS* 3
Spring 1 Core MSML604 PCS* 3
Spring 1 Core MSML605 PCS* 3
Spring 1 Elective MSML*** PCS* 3
Summer 1 Elective MSML*** PCS* 3
Fall 2 Core MSML606 PCS* 3
Fall 2 Elective MSML*** PCS* 3
Fall 2 Elective MSML*** PCS* 3
Semester Year Type Course Number Section Code Credits
Fall 1 Core MSML601 PCS* 3
Fall 1 Core MSML603 PCS* 3
Spring 1 Core MSML604 PCS* 3
Spring 1 Core MSML605 PCS* 3
Summer 1 Elective MSML*** PCS* 3
Summer 1 Elective MSML*** PCS* 3
Fall 2 Core MSML602 PCS* 3
Fall 2 Core MSML606 PCS* 3
Spring 2 Elective MSML*** PCS* 3
Spring 2 Elective MSML*** PCS* 3

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:

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 (Core)

MSML610: Advanced Machine Learning (Elective)

MSML612: Deep Learning (Elective)

MSML620: Estimation and Detection (Elective)

MSML621: Digital Signal Processing (Elective)

MSML630: Numerical Methods (Elective)

MSML640: Computer Vision (Elective)

MSML641: Natural Language Processing (Elective)

MSML642: Robotics (Elective)

MSML650: Cloud Computing (Elective)

MSML651: Big Data Analytics (Elective)

Questions? Contact Us