Courses recommended for machine learning graduate students

The computational statistics sequence

Background from mathematics, optimization and CS theory

CMSC 37000: Algorithms (Babai) Winter

Machine learning themed courses

STAT 37601/CMSC 25025: Machine Learning and Large Scale Data Analysis (Lafferty) Spring.
STAT 37400: Nonparametric Inference (Lafferty) Fall.
STAT 41500-41600: High Dimensional Statistics. Autumn/Spring.
STAT 37500: Pattern Recognition (Amit) Spring.
STAT 37750: Compressed Sensing (Foygel-Barber) Spring.
STAT 34000: Gaussian Processes (Stein) Spring.
TTIC 31180: Probabilistic Graphical Models (Walter) Spring.
TTIC 31120: Statistical and Computational Learning Theory (Srebro) Spring.


Note: Students must also take courses to satisfy their core degree requriements. For more detailed information see the CS, Stat and TTI course lists.