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Machine Learning
JUNE 22, 2026 — AUGUST 13, 2026
CS229
Details:
Time: No Topic - No Type
Units: 4
Class Number: 11456
Interest Area: Computer Science and Engineering
Instructor: 1 Staff
Population: Undergraduate, Graduate
Interest Area: Computer Science and Engineering
Course Format & Length: In Person, 8 Weeks
Pre-requisites:
Pre-requisites
We expect visiting students to have knowledge that is equivalent to the listed Stanford pre-requisite course.
Knowledge of basic computer science principles and skills at a level sufficient to write a reasonably non-trivial computer program in Python/NumPy to the equivalency of CS 106A, CS 106B, or CS 106X, familiarity with probability theory to the equivalency of CS 109, MATH 151, or STATS 116, and familiarity with multivariable calculus and linear algebra to the equivalency of MATH 51 or CS 205.
Cross Listing: STATS229 MACHINE LEARNING
Grading Basis: Letter or Credit/No Credit
Description:
Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, deep learning, model/feature selection, learning theory, ML advice, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search.
Course notes:
Stanford graduate students may enroll in 3 units; all other students must enroll in 4 units.
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