| LEC # | TOPICS |
|---|---|
| 1 | Introduction, linear classification, perceptron update rule (PDF) |
| 2 | Perceptron convergence, generalization (PDF) |
| 3 | Maximum margin classification (PDF) |
| 4 | Classification errors, regularization, logistic regression (PDF) |
| 5 | Linear regression, estimator bias and variance, active learning (PDF) |
| 6 | Active learning (cont.), non-linear predictions, kernals (PDF) |
| 7 | Kernal regression, kernels (PDF) |
| 8 | Support vector machine (SVM) and kernels, kernel optimization (PDF) |
| 9 | Model selection (PDF) |
| 10 | Model selection criteria (PDF) |
| 11 | Description length, feature selection (PDF) |
| 12 | Combining classifiers, boosting (PDF) |
| 13 | Boosting, margin, and complexity (PDF) |
| 14 | Margin and generalization, mixture models (PDF) |
| 15 | Mixtures and the expectation maximization (EM) algorithm (PDF) |
| 16 | EM, regularization, clustering (PDF) |
| 17 | Clustering (PDF) |
| 18 | Spectral clustering, Markov models (PDF) |
| 19 | Hidden Markov models (HMMs) (PDF) |
| 20 | HMMs (cont.) (PDF) |
| 21 | Bayesian networks (PDF) |
| 22 | Learning Bayesian networks (PDF) |
| 23 |
Probabilistic inference Guest lecture on collaborative filtering (PDF) |
| 24 | Current problems in machine learning, wrap up |
