Applied Machine Learning (SAML)
Graduate Degree Program
College: Computer, Mathematical, and Natural Sciences
ABSTRACT
The Master of Science in Applied Machine Learning offers students the opportunity to 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 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. The program consists of 30-credit course work and is a non-thesis MS program.
CONTACT
Science Academy
College of Computer, Mathematical, and Natural Sciences
3400 A.V. Williams
8223 Paint Branch Drive
University of Maryland
College Park, MD 20742
Email: scienceacademy@umd.edu
Phone: 301.405.9101
Website: https://cmns.umd.edu/graduate/science-academy/data-science/masters
GENERAL REQUIREMENTS
- Statement of Purpose
- Transcript(s)
- TOEFL/IELTS/PTE (international graduate students)
PROGRAM-SPECIFIC REQUIREMENTS
- Letter of Recommendation (1 optional)
- CV/Resume
- Description of Research/Work Experience
- Prior Coursework: Prior coursework establishing quantitative ability (i.e. calculus, linear algebra, basic statistics etc.).
- Proficiency in programming languages: Proficiency in programming languages, demonstrated either through prior programming coursework or substantial software development experience.
Type of Applicant | Fall Deadline | Spring Deadline |
---|---|---|
Domestic Applicants | ||
US Citizens and Permanent Residents | May 30, 2025 | N/A |
International Applicants | ||
F (student) or J (exchange visitor) visas; A,E,G,H,I and L visas and immigrants | February 28, 2025 | N/A |
RESOURCES AND LINKS:
Program Website: https://cmns.umd.edu/graduate/science-academy/data-science/masters
Application Process: www.gradschool.umd.edu/admissions