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1 Introduction
2 The PAC Learning Framework
3 Rademacher Complexity and VC Dimension
4 Model Selection
5 Support Vector Machines
6 Kernel Methods
7 Boosting
8 OnLine Learning
9 Multi Class Classification
10 Ranking
11 Regression
12 Maximum Entropy Models
13 Conditional Maximum Entropy Models
14 Algorithmic Stability
15 Dimensionality Reduction
16 Learning Automata and Languages
17 Reinforcement Learning
Conclusion
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