Stanford University · CS229

Machine Learning

机器学习

Supervised and unsupervised learning, learning theory, and reinforcement learning.

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Lecture 1: Machine learning overview

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Subject：Machine Learning

Level：Advanced

Suggested stage：Years 3–4 (inferred)

Public-material edition：Official course page (year not stated)

Language：English

Videos：Available

Assignments / solutions：Available / Not verified

Prerequisites

Programming →Probability →Linear algebra →

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▶Lectures, video & readingCourse content in the official sequence20

Lecture 1: Machine learning overview Lecture 2: Linear regression and gradient descent Lecture 3: Locally weighted regression and logistic regression Lecture 4: Generalized linear models Lecture 5: Generative learning algorithms Lecture 6: Neural networks and SVM margins Lecture 7: SVM duality and kernels Lecture 8: Soft-margin SVM and SMO Lecture 9: Bias, variance, and uniform convergence Lecture 10: VC dimension and model selection Lecture 11: Regularization and practical diagnostics Lecture 12: K-means and expectation maximization Lecture 13: Gaussian mixtures and factor analysis Lecture 14: PCA and dimensionality reduction Lecture 15: SVD and independent component analysis Lecture 16: MDPs and dynamic programming Lecture 17: Continuous-state reinforcement learning Lecture 18: LQR and dynamical systems Lecture 19: Kalman filtering and LQG Lecture 20: POMDPs and policy search

✓Assignments & practiceOpen the matching problem or assignment page4

Problem Set 1 Problem Set 2 Problem Set 3 Problem Set 4

◆Labs & projectsCourse projects, labs and milestones4

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Stanford Engineering Everywhere · verified 2026-07-31

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