Master of Professional Studies in Machine Learning
Winter Term begins November 25, 2019. The registration deadline for domestic students is October 18, 2019.
Fall Term begins August 31, 2020. The registration deadline for international students is March 13, 2020. Domestic students should apply by July 1, 2020 for best consideration. The registration deadline for domestic students is July 31, 2020.
About the Program
Acquire the skills and knowledge necessary for a career in today’s information-based society with the Master of Professional Studies in Machine Learning. This 30-credit, 10-course, non-thesis graduate program’s rigorous technical curriculum has been designed to prepare students for a career as an information engineer, data scientist, or data mining engineer. The MPS in Machine Learning focuses on the methods and techniques of creating models and algorithms that learn from, and make decisions or predictions, based on data. 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.
In the MPS in Machine Learning, students engage in cutting-edge technical course work in machine learning and develop their problem-solving skills in the art and science of processing and extracting information from data. Throughout their coursework, 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. The program also focuses on the applications of machine learning to computer vision, natural language processing, robotics, data science, and other areas. The MPS in Machine Learning is offered through the Science Academy in the College of Computer, Mathematical, and Natural Sciences.
The MPS in Machine Learning is a 30-credit graduate program is designed to accommodate working professionals while minimally disrupting personal and professional life. Instruction is provided by UMD faculty and experts in the field. The program features face-to-face instructional delivery; classes meet at the UMD College Park campus. The MPS in Machine Learning uses the term academic calendar: in a 12-week term, students complete course work in 11 weeks; week 12 is reserved for finals.
Plan of Study & Courses
The MPS 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. The degree requirements consists of successful completion of 6 core courses, 4 elective courses, and the acceptance of a scholarly paper.
Suggested Plan of Study (full time; two 3-credit courses per term):
Term 1: MSML601 Probability and Statistics (Core)
MSML603 Principles of Machine Learning (Core)
Term 2: MSML604 Introduction to Optimization (Core)
MSML605 Computing Systems for Machine Learning (Core)
Term 3: MSML606 Algorithms and Data Structures for Machine Learning (Core)
MSML610 Advanced Machine Learning (Elective)
Term 4: MSML602 Principles of Data Science (Core)
MSML612 Deep Learning (Elective)
Term 5: MSML640 Computer Vision (Elective)
MSML641 Natural Language Processing (Elective)
Any student applying for admission to a graduate program at the University of Maryland must meet the following minimum admission criteria as established by the Graduate School.
Applicants must have earned a four-year baccalaureate degree from a regionally accredited U.S. institution, or an equivalent degree from a non-U.S. institution.
Applicants must have earned a 3.0 GPA (on a 4.0 scale) in all prior undergraduate and graduate coursework.
Applicants must provide an official copy of a transcript for all of their post-secondary work.
Statement of Purpose
TOEFL/IELTS/PTE (international graduate students)
Graduate Record Examination (GRE) (optional)
Description of research/work experience
Prior coursework establishing quantitative ability (i.e. calculus, linear algebra, basic statistics, etc.)
Proficiency in programming languages, demonstrated either through prior programming coursework or substantial software development experience
Tuition and Fees