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Topics covered
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Lecture 1: Introduction, maximum likelihood estimation
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Lecture 2: linear regression, least squares, geometric view
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Lecture 3: ridge regression, probabilistic views of linear
regression
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Lecture 4: bias-variance, Bayes rule, MAP inference
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Lecture 5: Bayesian linear regression
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Lecture 6: sparsity, subset selection for linear regression
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Lecture 7: nearest neighbor classification, Bayes classifiers
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Lecture 8: linear classifiers, perceptron
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Lecture 9: logistic regression, Laplace approximation
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Lecture 10: kernel methods, Gaussian processes
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Lecture 11: maximum margin, support vector machines
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Lecture 12: trees, random forests
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Lecture 13: boosting
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Lecture 14: clustering, k-means
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Lecture 15: EM algorithm, missing data
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Lecture 16: mixtures of Gaussians
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Lecture 17: matrix factorization
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Lecture 18: non-negative matrix factorization
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Lecture 19: latent factor models, PCA and variations
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Lecture 20: Markov models
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Lecture 21: hidden Markov models
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Lecture 22: continuous state-space models
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Lecture 23: association analysis
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