the elements of statistical learning

Main topics covered

1. Supervised learning
2. Linear regression
3. Classification
4. Model assessment and selection
5. Basis expansions and regularization
6. Kernel methods
7. Decision trees
8. Bagging and random forests
9. Boosting
10. Neural networks
11. Support vector machines
12. Unsupervised learning
13. High-dimensional data analysis

Mathematical level

The book assumes familiarity with:

• Calculus
• Linear algebra
• Probability
• Mathematical statistics

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates.