About the Book:
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.
Table of Contents
IntroductionLinear DiscriminantsThe Multi-Layer PerceptronRadial Basis Functions and SplinesSupport Vector MachinesLearning with TreesDecision by Committee: Ensemble LearningProbability and LearningDimensionality ReductionOptimization and SearchEvolutionary Learning.Reinforcement LearningMarkov Chain Monte Carlo (MCMC) MethodsGraphical Models
