Master of Science in Machine Learning Curriculum for Students who Accepted before December 31, 2018
The curriculum for the Master's in Machine Learning requires 4 Set Core courses, 2 Menu Core courses, 3 electives or research, and a practicum.
MS students take all four Set Core courses:
- 10-701 Introduction to Machine Learning or 10-715 Advanced Introduction to Machine Learning
- 10-716 Advanced Machine Learning: Theory and Methods (formerly 10-702 Statistical Machine Learning)
- 10-718 Data Analysis
- 36-700 Probability & Mathematical Statistics or 36-705 Intermediate Statistics
Students take their choice of two Menu Core courses:
- 10-703 Deep Reinforcement Learning or 10-707 Topics in Deep Learning
- 10-708 Probabilistic Graphical Models
- 10-725 Convex Optimization
- 15-750 Algorithms or 15-853 Algorithms in the Real World
- 15-780 Graduate Artificial Intelligence
- 15-826 Multimedia Databases and Data Mining or 10-805 Machine Learning with Large Datasets
- 36-709/36-752 Advanced Probability or 36-710/36-755 Advanced Statistical Theory
- 36-707 Regression Analysis
Note: The two Menu Core courses must be taken from separate lines. E.g., a student may not use both 15-750 Algorithms and 15-853 Algorithms in the Real World to satisfy their Menu Core requirements.
Electives and Research
Students take three electives, which can be any 12-unit course from the School of Computer Science or Department of Statistics & Data Science at the 700-level or above, including additional courses from the Menu Core. Additional examples of courses of interest can be found on the Master's Electives page.
Students may also choose to replace one or two of the electives with research under a Machine Learning Core Faculty Member. Students should register for 10-620 Independent Study, with 12 units = 1 elective.
MS students also complete a practicum (an internship or research related to Machine Learning), generally conducted during the summer.