| 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)
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| 24 |
Current problems in machine learning, wrap up |