- Supervised and Unsupervised Learning
- Regression
- Linear Classification (including LDA)
- Kernel Methods
- Evaluation and Assessment (including the right and wrong way to do cross-validation)
- Bayesian inference
- Decision Trees and boosting methods
- Neural Nets
- SVMs
- K-Means clustering and nearest neighbor classification
- Random Forests
- Ensemble learning (shown to be very effective in the Netflix competition)
- Undirected Graphical Models (including RBMs)
While you're looking at books, another book to check out is Probabilistic Graphical Models.
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