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Stanford CS229 Machine Learning I Introduction I 2022 I Lecture 1
Stanford CS229 Machine Learning I Supervised learning setup, LMS I 2022 I Lecture 2
Stanford CS229 I Weighted Least Squares, Logistic regression, Newton's Method I 2022 I Lecture 3
Stanford CS229 Machine Learning I Exponential family, Generalized Linear Models I 2022 I Lecture 4
Stanford CS229 Machine Learning I Gaussian discriminant analysis, Naive Bayes I 2022 I Lecture 5
Stanford CS229 Machine Learning I Naive Bayes, Laplace Smoothing I 2022 I Lecture 6
Stanford CS229 Machine Learning I Kernels I 2022 I Lecture 7
Stanford CS229 Machine Learning I Neural Networks 1 I 2022 I Lecture 8
Stanford CS229 Machine Learning I Neural Networks 2 (backprop) I 2022 I Lecture 9
Stanford CS229 Machine Learning I Bias - Variance, Regularization I 2022 I Lecture 10
Stanford CS229 Machine Learning I Feature / Model selection, ML Advice I 2022 I Lecture 11
Stanford CS229 I K-Means, GMM (non EM), Expectation Maximization I 2022 I Lecture 12
Stanford CS229 Machine Learning I GMM (EM) I 2022 I Lecture 13
Stanford CS229 Machine Learning I Factor Analysis/PCA I 2022 I Lecture 14
Stanford CS229 Machine Learning I PCA/ICA I 2022 I Lecture 15
Stanford CS229 Machine Learning I Self-supervised learning I 2022 I Lecture 16
Stanford CS229 I Basic concepts in RL, Value iteration, Policy iteration I 2022 I Lecture 17
Stanford CS229 I Societal impact of ML (Guest lecture by Prof. James Zou) I 2022 I Lecture 18
Stanford CS229 Machine Learning I Model-based RL, Value function approximator I 2022 I Lecture 20
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