Book description
This bestseller helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation. Along with improved Python code, this second edition includes two new chapters on deep belief networks and Gaussian processes. It incorporates new material on the support vector machine, random forests, the perceptron convergence theorem, filters, and more. All of the code is available on the author's website.
Table of contents
- Preliminaries
- Prologue to 2nd Edition
- Prologue to 1st Edition
- Chapter 1: Introduction
- Chapter 2: Preliminaries
- Chapter 3: Neurons, Neural Networks, and Linear Discriminants
- Chapter 4: The Multi-layer Perceptron
- Chapter 5: Radial Basis Functions and Splines
- Chapter 6: Dimensionality Reduction
- Chapter 7: Probabilistic Learning
- Chapter 8: Support Vector Machines
- Chapter 9: Optimisation and Search
- Chapter 10: Evolutionary Learning
- Chapter 11: Reinforcement Learning
- Chapter 12: Learning with Trees
- Chapter 13: Decision by Committee: Ensemble Learning
- Chapter 14: Unsupervised Learning
- Chapter 15: Markov Chain Monte Carlo (MCMC) Methods
- Chapter 16: Graphical Models
- Chapter 17: Symmetric Weights and Deep Belief Networks
- Chapter 18: Gaussian Processes
- Appendix A: Python
Product information
- Title: Machine Learning, 2nd Edition
- Author(s):
- Release date: October 2014
- Publisher(s): Chapman and Hall/CRC
- ISBN: 9781498759786
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