Machine Learning: An Algorithmic Perspective, Second EditionA Proven, Hands-On Approach for Students without a Strong Statistical FoundationSince the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students |
Contents
| 1 | |
Chapter 2 Preliminaries | 15 |
Chapter 3 Neurons Neural Networks and Linear Discriminants | 39 |
Chapter 4 The MultiLayer Perceptron | 71 |
Chapter 5 Radial Basis Functions and Splines | 111 |
Chapter 6 Dimensionality Reduction | 129 |
Chapter 7 Probabilistic Learning | 153 |
Chapter 8 Support Vector Machines | 169 |
Chapter 12 Learning with Trees | 249 |
Ensemble Learning | 267 |
Chapter 14 Unsupervised Learning | 281 |
Chapter 15 Markov Chain Monte Carlo MCMC Methods | 305 |
Chapter 16 Graphical Models | 321 |
Chapter 17 Symmetric Weights and Deep Belief Networks | 359 |
Chapter 18 Gaussian Processes | 395 |
Appendix A Python | 415 |
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MACHINE LEARNING: An Algorithmic Perspective, Second Edition STEPHEN. MARSLAND No preview available - 2023 |
Common terms and phrases
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