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Machine Learning

Stanford CS229, Andrew Ng · AI · Fall 2026 · Planned

Lectures

  1. Lecture 1: Course Overview and What Machine Learning Is (1:15:20)
  2. Lecture 2: Linear Regression and Gradient Descent (1:18:17)
  3. Lecture 3: Locally Weighted and Logistic Regression (1:19:34)
  4. Lecture 4: Perceptron, GLMs, and Softmax Regression (1:22:01)
  5. Lecture 5: GDA and Naive Bayes (1:18:52)
  6. Lecture 6: Laplace Smoothing and Support Vector Machines (1:20:57)
  7. Lecture 7: Kernels and the Support Vector Machine (1:20:24)
  8. Lecture 8: Bias, Variance, and Model Selection (1:23:26)
  9. Discussion Section: Learning Theory (1:26:02)
  10. Lecture 9: Decision Trees and Ensemble Methods (1:20:41)
  11. Lecture 10: Introduction to Neural Networks (1:20:14)
  12. Lecture 11: Backprop and Improving Neural Networks (1:16:37)
  13. Lecture 12: Debugging ML Models and Error Analysis (1:18:54)
  14. Lecture 13: Expectation-Maximization Algorithms (1:20:31)
  15. Lecture 14: EM Algorithm and Factor Analysis (1:19:47)
  16. Lecture 15: PCA and ICA (1:18:35)
  17. Lecture 16: Independent Component Analysis & Reinforcement Learning (1:18:10)
  18. Lecture 17: MDPs, Value Iteration and Policy Iteration (1:19:14)
  19. Lecture 18: Continuous-State MDPs and Fitted Value Iteration (1:20:14)
  20. Lecture 19: State-Action Rewards, Finite-Horizon MDPs and LQR (1:21:06)
  21. Lecture 20: RL Debugging, Diagnostics, and Policy Search (1:12:43)

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