Courses recommended for machine learning graduate students
The computational statistics sequence
STAT 37710 / CMSC 35400: Machine Learning (Kondor) Spring.
Background from mathematics, optimization and CS theory
TTIC 31150/CMSC 31150: Mathematical Toolkit (Tulsiani) Fall
TTIC 31070: Convex Optimization (Srebro) Fall
CMSC 37000: Algorithms (Babai) Winter
TTIC 31080: Approximation Algorithms (Chuzhoy) Spring
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.
Applications
TTIC 31190: Natural Language Processing (Gimpel) Winter.
TTIC 31050: Intro to Bioinformatics (Xu) Winter.
TTIC 31040: Intro to Computer Vision (McAllester) Winter.
TTIC 31110: Speech Technologies (Livescu) 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.