Program Overview
School of Computer Science- Location: Pittsburgh, PA
- Length: 16 months
- Tuition: $90,000
- Website: https://www.ml.cmu.edu/
The Master’s in Machine Learning at Carnegie Mellon University is a full-time, on-campus graduate program designed for individuals with strong backgrounds in mathematics, statistics, and computer science. Established in 2006 within the world’s first academic machine learning department, the program focuses on both theoretical foundations and real-world applications of machine learning.
Curriculum and Structure
The program consists of 6 core courses, 3 electives, and a practicum, typically completed over three to four semesters. Coursework includes foundational topics such as statistical learning, machine learning theory, and practical applications in areas like computer vision and natural language processing.
10-701 Introduction to Machine Learning
Covers core machine learning principles including algorithms, theory, and mathematical foundations. Emphasizes adaptive systems that improve through experience across applications like NLP, robotics, and statistics. Designed for PhD-level students with strong math and CS backgrounds.
10-617 Intermediate Deep Learning
Explores deep learning architectures such as CNNs, RNNs, and generative models. Combines theory with applications in vision, language, and audio. Requires solid programming and mathematical skills to understand supervised and unsupervised techniques.
10-708 Probabilistic Graphical Models
Introduces Bayesian and Markov networks for modeling complex systems. Focuses on inference, structure learning, and parameter estimation. Equips students to build and apply graphical models to real-world datasets in AI and related fields.
10-718 Machine Learning in Practice
Applies machine learning concepts to real-world problems through hands-on projects. Focuses on bridging theory and implementation with a strong emphasis on debugging, testing, and iteration. Encourages applied learning using real data and systems.
10-725 Optimization for Machine Learning (formerly Convex Optimization)
Covers optimization techniques with a focus on convex problems commonly found in ML. Emphasizes problem structure, algorithms, and real-world ML applications. Includes a semester-long project applying optimization methods.
36-700 Probability and Mathematical Statistics
Provides a foundation in probability and statistical theory. Covers estimation, hypothesis testing, asymptotics, and Bayesian methods. Requires calculus and linear algebra; suitable for graduate students seeking applied statistical training.
The practicum, held during the summer term, allows students to gain hands-on experience through internships or research.
See course catalog for more information: http://coursecatalog.web.cmu.edu/schools-colleges/schoolofcomputerscience/courses/
Program Duration and Delivery
The program is designed to be completed in 16 months but may extend to four semesters based on student preparedness. It is not available online; all students must attend in person on the Pittsburgh campus.
Tuition and Costs
The tuition for master’s programs is generally $54,000 to $60,000 per year. Since the MS in Machine Learning is a 16-month program, the total tuition is approximately $80,000–$90,000, depending on course load and fee structure.
More tuition details here: https://www.ml.cmu.edu/academics/ms-finance.html
Admissions and Requirements
Applicants must demonstrate strong programming ability and a solid grasp of probability, statistics, linear algebra, and multivariate calculus. A computer science undergraduate degree is not required, but relevant coursework and experience are essential. GRE scores are optional for Fall 2025 admissions.
Career Outcomes
Graduates of the MSML program are well-prepared for high-level roles in industry or academia. Common job titles include Machine Learning Engineer, Data Scientist, and AI Researcher. CMU’s Career and Professional Development Center provides post-graduate destination data.
Ideal Candidates
This program is best suited for students with strong analytical skills and prior exposure to statistical computing and algorithmic design. It serves those aiming to develop advanced expertise for roles in research labs, AI startups, or large tech companies.
The Carnegie Mellon Master’s in Machine Learning is best suited for applicants with strong foundations in mathematics and computer science who want to deepen their expertise in statistical and algorithmic approaches to machine learning. This program is ideal for:
1. Candidates with Strong Quantitative Backgrounds
The program expects incoming students to have completed coursework in:
- Probability and statistics (at least one year of college-level study)
- Multivariate calculus and linear algebra
- Programming (experience with Python, Java, Matlab, or R is beneficial)
2. Students Seeking Specialized, On-Campus Training
This 16-month, full-time, in-person program based in Pittsburgh requires physical attendance. It is not available online and is intended for those committed to immersive academic work and hands-on practicum or research experience.
3. Professionals or Recent Graduates Pursuing Technical Depth in ML
Students should be prepared to take six rigorous core courses, three electives, and complete a summer practicum. A typical student profile includes:
- GPA near 3.9/4.0
- High GRE Quantitative scores (average: 169)
- Prior academic or research experience in machine learning, statistics, or computational theory
4. Applicants Focused on Research and Real-World Impact
The department is deeply involved in applied ML research, including:
- Epidemiological forecasting
- Reinforcement learning for societal decision-making
- AI fairness, robustness, and explainability Students benefit from access to CMU’s extensive research labs and a new partnership with Google for large-scale GPU cloud resources.
5. International Students or U.S.-based Students Committed to STEM Careers
The program is STEM-designated and supports F-1 visas. However, it requires full-time study and completion within three semesters for international students.
Additional Offerings
CMU’s Machine Learning Department also offers joint Ph.D. programs, a 5th-Year Master’s program for CMU undergraduates, and an undergraduate minor in machine learning. These programs share faculty and resources to maintain academic cohesion.
Conclusion
The Master’s in Machine Learning at Carnegie Mellon is a rigorous, research-driven program built for students ready to contribute meaningfully to the development and application of machine learning systems. Its unique history, top-tier faculty, and research integration make it one of the most competitive programs globally in the field.