Slides
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Topics
covered
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PDF
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Introduction, maximum likelihood estimation
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linear regression, least squares, geometric view
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ridge regression, probabilistic views of linear regression
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bias-variance, Bayes rule, maximum a posteriori
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Bayesian linear regression
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sparsity, subset selection for linear regression
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nearest neighbor classification, Bayes classifiers
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linear classifiers, perceptron |
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logistic regression, Laplace approximation |
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kernel methods, Gaussian processes |
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maximum margin, support vector machines |
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trees, random forests |
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boosting
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PDF
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clustering, k-means
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EM algorithm, missing data
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mixtures of Gaussians
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matrix factorization
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non-negative matrix factorization
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latent factor models, PCA and variations
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Markov models
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hidden Markov models
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continuous state-space models
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association analysis
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model selection
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