Program Overview
Science Academy- Location: College Park, MD
- Length: 24 months
- Tuition: $41,460
- Website: https://cmns.umd.edu/graduate/science-academy/machine-learning
The MS in Applied Machine Learning at the University of Maryland is a 30-credit, 10-course, non-thesis graduate program designed to prepare students for roles such as data scientist, machine learning engineer, or information engineer.
This program is focused on applied techniques for data-driven modeling and prediction across industries such as healthcare, finance, telecommunications, and engineering.
Duration and Delivery
Students can complete the program in under two years, either full-time or part-time. Courses are delivered in person at the College Park campus, with most classes held in the evening to support working professionals.
Curriculum Structure
The curriculum includes 6 core courses and 4 electives. Core subjects cover probability, optimization, computing systems, algorithms, and principles of machine learning and data science.
MSML601: Probability and Statistics
Covers core probability and statistical concepts including distributions, random variables, expectation, covariance, and Bayes’ theorem. Students explore limit theorems, stochastic processes, and fundamentals of estimation and hypothesis testing.
MSML602: Principles of Data Science
Introduces the data science lifecycle from raw data to insights, emphasizing tools, systems, and case studies used in modern data analysis pipelines.
MSML603: Principles of Machine Learning
Presents key supervised and unsupervised learning methods, including SVMs, neural networks, clustering, and PCA. Includes real-world applications such as vision, navigation, and speech.
MSML604: Introduction to Optimization
Focuses on solving optimization problems through methods like gradient descent, Newton’s method, and global search. Covers linear algebra review, convexity, duality, and constraints.
MSML605: Computing Systems for Machine Learning
Examines software and hardware components of ML systems. Topics include Python programming, object-oriented design, GPU architecture, parallel computing, and system-level ML implementation.
MSML606: Algorithms and Data Structures for Machine Learning
Explores essential data structures and algorithms, including sorting, searching, graph algorithms, and dynamic programming. Emphasizes ML-focused applications and algorithm complexity analysis.
Electives include deep learning, natural language processing, robotics, big data analytics, and computer vision.
MSML612: Deep Learning
Explores multi-layer neural networks and key concepts such as backpropagation, convolutional networks, and generative models. Includes practical applications in vision and language tasks.
MSML621: Digital Signal Processing
Introduces DSP concepts and wireless communication applications. Covers signal detection, filtering, FFTs, and integrates ML approaches using hands-on radio hardware.
MSML640: Computer Vision
Covers foundational vision topics such as filtering, edge detection, 3D reconstruction, and deep learning for object recognition in images and video.
MSML641: Natural Language Processing
Explores computational methods for understanding language. Topics include n-gram models, HMMs, part-of-speech tagging, parsing, translation, and speech synthesis.
MSML642: Robotics
Covers robotics fundamentals including motion dynamics, control systems, sensors, and planning. Highlights machine learning applications in grasping and autonomous motion.
MSML650: Cloud Computing
Focuses on modern cloud technologies, including virtualization, containerization, orchestration, security, and quality of service in scalable networked environments.
MSML651: Big Data Analytics
Covers scalable ML methods for large datasets, including streaming, parallel computing, MapReduce, and database systems. Emphasizes performance, storage, and visualization tools.
See here for more course details.
Academic Requirements
Applicants must hold a four-year bachelor’s degree with a minimum GPA of 3.0. They should demonstrate strong quantitative ability through prior coursework in areas like calculus II, linear algebra, and statistics, as well as proficiency in programming. GRE scores are optional.
Program Costs
The program charges $4,146 per course, totaling approximately $41,460 for all 10 courses. Additional fees include semester-based graduate student and campus service fees, bringing the total cost slightly higher depending on course load and term.
More tuition details here:
Learning Outcomes
Graduates will develop expertise in designing, implementing, and applying machine learning algorithms to practical problems. The program emphasizes hands-on experience and technical skills over research or theoretical exploration.
Best Fit for Students
This program is ideal for early-career professionals or recent graduates with a solid foundation in mathematics and programming who want to build practical ML skills for use in industry. It is not suited for students seeking a research-intensive or online program.
Conclusion
The MS in Applied Machine Learning provides a focused, accessible path to applied machine learning careers through a rigorous curriculum, experienced faculty, and real-world applications—all delivered in a flexible evening class format at the University of Maryland.