Introduction To Machine Learning
by Nils J Nilsson
Number of pages: 209
This book surveys many of the important topics in machine learning circa 1996. The intention was to pursue a middle ground between theory and practice. This book concentrates on the important ideas in machine learning -- it is neither a handbook of practice nor a compendium of theoretical proofs. The goal was to give the reader sufficient preparation to make the extensive literature on machine learning accessible.
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by D. Michie, D. J. Spiegelhalter - Ellis Horwood
The book provides a review of different approaches to classification, compares their performance on challenging data-sets, and draws conclusions on their applicability to realistic industrial problems. A wide variety of approaches has been taken.
by Gianluca Bontempi, Souhaib Ben Taieb - OTexts
This handbook aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. This manuscript aims to find a good balance between theory and practice.
by Hal Daumé III - ciml.info
Tis is a set of introductory materials that covers most major aspects of modern machine learning (supervised and unsupervised learning, large margin methods, probabilistic modeling, etc.). It's focus is on broad applications with a rigorous backbone.
by Amnon Shashua - arXiv
Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).