Machine Learning: An Algorithmic Perspective

Introduction to Machine Learning introduces this subject to computer science students and others who may not have a strong mathematical background. Focusing on algorithms and applications, the text presents three distinct sets of problems for each section: standard questions that test understanding of the material, structured programming exercises using code and data from the Internet, and suggested further investigations, often involving some basic programming. The book covers such fundamental topics as neuronal modeling, perceptron, multi-layer perceptron, classification, regression, decision trees, the naive Bayes' classifier, unsupervised learning, the self-organizing map, and genetic algorithms.