Metadata
Title
Carnegie Mellon University School of Computer Science
Category
courses
UUID
7cdbd00799cb4afab031b317a3d0b395
Source URL
https://ml.cmu.edu/academics/machine-learning-masters-curriculum
Parent URL
https://www.cs.cmu.edu/academics/overview-programs
Crawl Time
2026-03-25T05:25:49+00:00
Rendered Raw Markdown

Carnegie Mellon University School of Computer Science

Source: https://ml.cmu.edu/academics/machine-learning-masters-curriculum Parent: https://www.cs.cmu.edu/academics/overview-programs

Home

Academics Master's in Machine Learning Curriculum

The Master of Science in Machine Learning offers students with a bachelor's degree the opportunity to improve their training with advanced study in machine learning. Incoming students should have good analytical skills and a strong aptitude for mathematics, statistics and programming.\

The program consists primarily of coursework, although students do have the chance to engage in research. Contact us with questions and concerns. 

Machine Learning Minor

Machine learning and statistical methods are increasingly used in many application areas including natural language processing, speech, vision, robotics, and computational biology. The Minor in Machine Learning allows undergraduates to learn about the core principles of machine learning.

The curriculum varies based on when students began their undergraduate program at CMU:

Curriculum for 2018 and earlier\ Curriculum for 2019 and later

Machine Learning Concentration

Students within the School of Computer Science can add the Machine Learning Concentration to their major to enhance their computer science education.

Statistics & Machine Learning Major

This joint major, managed by the Dietrich College of Humanities and Social Sciences, develops the critical ideas and skills underlying statistical machine learning — the creation and study of algorithms that enable systems to automatically learn and improve with experience. It is ideal for students interested in statistical computation, data science, or "Big Data" problems, including those planning to pursue a related PhD or a job in the tech industry.\

Bachelor's of Science in Artificial Intelligence

Carnegie Mellon has led the world in artificial intelligence education and innovation since the field was created. It's only natural, then, that the School of Computer Science would offer the nation's first bachelor's degree in artificial intelligence, which we introduced in fall 2018. A B.S. in AI from Carnegie Mellon University, unites disciplines from machine learning to natural language processing, instruction in the BSAI program includes faculty members from the school's Computer Science Department, Human-Computer Interaction Institute, Institute for Software Research, Language Technologies Institute, Machine Learning Department and Robotics Institute.

Courses in Machine Learning

These courses are being offered by the Machine Learning Department this semester.

Teaching Assistantships

Apply to be a Teaching Assistant or Course Assistant in the Machine Learning Department. Both graduate and undergraduate students are welcome to apply.

Curriculum

The curriculum for the master's degree in machine learning requires six core courses, three elective courses and a practicum.

Core

M.S. students take all six core courses:

Note: The core courses must be taken from separate lines. For example, a student may not use both 10-703: Deep Reinforcement Learning and 10-707: Advanced Deep Learning to satisfy their core requirements.

Electives

Students take their choice of three elective courses (from separate lines):

Notes

If a student takes both 10-703: Deep Reinforcement Learning and 10-707: Advanced Deep Learning, one will count for the core and the other will count as an elective.

A student may fulfill one, two, or three electives with Independent Study, if desired. The most common arrangement is one research project conducted over two semesters (counting as two electives), since it takes time to get up to speed on a new research project. But a project may be as short as one semester or as long as three semesters plus the summer practicum. Depending on the project(s), it's possible to do research under different faculty in different semesters, but only one independent study can be completed at a time.

Multiple Special Topics in Machine Learning courses can be used as electives; it is not limited to one Special Topics course per student. These courses will generally have 10-XXX course numbers, but not all 10-XXX courses are approved as electives. To know if a specific course counts as an elective, consult the list below or email the MSML Programs Manager.

Examples of Special Topics Courses

Practicum

M.S. students also complete a one-semester, full-time practicum (an internship or research related to machine learning), generally during the summer.