Applied Machine Learning - Master of Science
Acquire the skills and knowledge necessary for a career in today’s information-based society with the Master of Science in Applied Machine Learning. This 30-credit, 10-course, non-thesis graduate program’s rigorous technical curriculum is designed to prepare students for a career as an information engineer, data scientist, or data mining engineer. The MS in Applied 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 MS in Applied Machine Learning, 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. 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 MS in Applied Machine Learning is offered through the Science Academy in the College of Computer, Mathematical, and Natural Sciences.
The MS in Applied Machine Learning is a 30-credit graduate program designed for to accommodate working professionals and can be completed in less than two years. The program emphasizes practical knowledge and does not offer research opportunities. 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, mostly in the evenings.
Program Directors & Instructors
Amol Deshpande
Co-Director, Master of Science in Applied Machine Learning
Sennur Ulukus
Co-Director, Master of Science in Applied Machine Learning
Samet Ayhan
Lecturer
Babak Azimi-Sadjadi
Lecturer
Kemal Davaslioglu
Lecturer
Leonid Koralov
Professor, Mathematics
Richard La
Professor, Department of Electrical and Computer Engineering
Alejandra Mercado
Associate Director, Master's in Telecommunications Program, Electrical and Computer Engineering
Tammy Perrin
Lecturer
Philip Resnik
Professor, Linguistics and UMIACS
Zoltan Safar
Director, Electrical and Computer Engineering-Telecommunications Program
