Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010336556 | Q325.5 S534 2014 | Open Access Book | Book | Searching... |
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Summary
Summary
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
Table of Contents
| 1 Introduction |
| Part I Foundations |
| 2 A gentle start |
| 3 A formal learning model |
| 4 Learning via uniform convergence |
| 5 The bias-complexity trade-off |
| 6 The VC-dimension |
| 7 Non-uniform learnability |
| 8 The runtime of learning |
| Part II From Theory to Algorithms |
| 9 Linear predictors |
| 10 Boosting |
| 11 Model selection and validation |
| 12 Convex learning problems |
| 13 Regularization and stability |
| 14 Stochastic gradient descent |
| 15 Support vector machines |
| 16 Kernel methods |
| 17 Multiclass, ranking, and complex prediction problems |
| 18 Decision trees |
| 19 Nearest neighbor |
| 20 Neural networks |
| Part III Additional Learning Models |
| 21 Online learning |
| 22 Clustering |
| 23 Dimensionality reduction |
| 24 Generative models |
| 25 Feature selection and generation |
| Part IV Advanced Theory |
| 26 Rademacher complexities |
| 27 Covering numbers |
| 28 Proof of the fundamental theorem of learning theory |
| 29 Multiclass learnability |
| 30 Compression bounds |
| 31 PAC-Bayes |
| Appendix A Technical lemmas |
| Appendix B Measure concentration |
| Appendix C Linear algebra |
