Machine Learning: An Algorithmic Perspective, 2nd Edition

A Proven, Hands-On Approach for Students without a Strong Statistical Foundation

Since 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 without a strong statistical background often find it hard to get started in this area.

Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition 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.

New to the Second Edition

• Two new chapters on deep belief networks and Gaussian processes
• Reorganization of the chapters to make a more natural flow of content
• Revision of the support vector machine material, including a simple implementation for experiments
• New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron
• Additional discussions of the Kalman and particle filters
• Improved code, including better use of naming conventions in Python

Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website.

Table of Contents

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 andSplines
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 DeepBelief Networks
Chapter 18: Gaussian Processes