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