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 working professionals and can be completed in less than two years.  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. The Science Academy has run a Master of Professional Studies (MPS) degree since Fall 2019. Current MPS students should follow the MS curriculum plan of study and contact the Science Academy with any advising questions or concerns. 

Application Deadlines

Fall 2025
Applications will be available in late summer 2024

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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.

 

General Requirements:

  • Statement of Purpose
  • Transcript(s)
  • TOEFL/IELTS/PTE (international graduate students)

 

Program-Specific Requirements:

  • Graduate Record Examination (GRE) (optional)
  • CV/Resume
  • Description of research/work experience
  • Prior coursework establishing quantitative ability (including calculus II, linear algebra, statistics, etc.)
  • Proficiency in programming languages, demonstrated either through prior programming coursework or substantial software development experience

The MS in Applied 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 consist of successful completion of 6 core courses and 4 elective courses.

Sample Plan of Study (Part-time, two 3-credit courses per semester)

Semester 1 (fall)
  • MSML601 Probability and Statistics (Core)
  • MSML603 Principles of Machine Learning (Core)
Semester 2 (spring)
  • MSML604 Introduction to Optimization (Core)
  • MSML605 Computing Systems for Machine Learning (Core)
Semester 3 (summer)
  • MSML612: Deep Learning (Elective)
  • MSML640 Computer Vision (Elective)
Semester 4 (fall)
  • MSML602 Principles of Data Science (Core)
  • MSML606 Algorithms and Data Structures for Machine Learning (Core)
Semester 5 (spring)
  • MSML641 Natural Language Processing (Elective)
  • MSML610 Advanced Machine Learning (Elective)

Sample Plan of Study (Full-time, three 3-credit courses per semester)

Semester 1 (fall)
  • MSML601 Probability and Statistics (Core)
  • MSML602 Principles of Data Science (Core)
  • MSML603 Principles of Machine Learning (Core)
Semester 2 (spring)
  • MSML604 Introduction to Optimization (Core)
  • MSML605 Computing Systems for Machine Learning (Core)
  • MSML641 Natural Language Processing (Elective)
Semester 3 (summer)
  • MSML612: Deep Learning (Elective)
Semester 4 (fall)
  • MSML606 Algorithms and Data Structures for Machine Learning (Core)
  • MSML650: Cloud Computing (Elective)
  • MSML651: Big Data Analytics (Elective)

 

Learn more about the courses

Find up to date tuition and fee information here for the MS in Applied Machine Learning.

Program Directors & Instructors

Co-Director, Machine Learning Master of Professional Studies Program
Co-Director, Machine Learning Master of Professional Studies Program
Associate Professor, Electrical and Computer Engineering
Professor, Mathematics
Professor, Department of Electrical and Computer Engineering
Adjunct Assistant Professor, Computer Science
Associate Director, Master's in Telecommunications Program, Electrical and Computer Engineering
Professor, Linguistics and UMIACS
Director, Electrical and Computer Engineering-Telecommunications Program