International Deadline : February 26, 2027 Domestic Deadline : May 28, 2027 Admission Requirements 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 transcripts 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 (Python, MATLAB, R preferred), demonstrated either through prior programming coursework or substantial software development experience Plan of Study & Courses 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 MSML601 Probability and Statistics (Core) MSML603 Principles of Machine Learning (Core) Semester 2 MSML604 Introduction to Optimization (Core) MSML605 Computing Systems for Machine Learning (Core) Semester 3 MSML Elective OR MSML606 Algorithms and Data Structures for Machine Learning (Core) MSML Elective Semester 4 MSML602 Principles of Data Science (Core) MSML Elective Semester 5 MSML Elective OR MSML606 Algorithms and Data Structures for Machine Learning (Core) MSML Elective Sample Plan of Study (Full-time, three 3-credit courses per semester) Semester 1 MSML601 Probability and Statistics (Core) MSML602 Principles of Data Science (Core) MSML603 Principles of Machine Learning (Core) Semester 2 MSML604 Introduction to Optimization (Core) MSML605 Computing Systems for Machine Learning (Core) MSML606 Algorithms and Data Structures for Machine Learning (Core) Semester 3 MSML Elective MSML Elective MSML Elective Semester 4 MSML Elective Electives Include: MSML612: Deep Learning MSML640: Computer Vision MSML641: Natural Language Processing MSML642: Robotics MSML650: Cloud Computing MSML651: Big Data Analytics Tuition & Fees Up-to-date tuition and fee information for the MS in Applied Machine Learning.
| QS 综合 | 院校 | 学制 | 学费 |
|---|---|---|---|
| 第 55 位 | 卡内基梅隆大学Carnegie Mellon University | 两年 | 52,320美元 |
| 第 82 位 | 瑞典皇家理工学院KTH Royal Institute of Technology | 2年（120 ECTS） | - |