Introduction to Machine Learning
by Amnon Shashua
Publisher: arXiv 2009
Number of pages: 109
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).
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by Aaron Hertzmann - University of Toronto
Contents: Introduction to Machine Learning; Linear Regression; Nonlinear Regression; Quadratics; Basic Probability Theory; Probability Density Functions; Estimation; Classification; Gradient Descent; Cross Validation; Bayesian Methods; and more.
by Albert Bifet, et al. - The MIT Press
This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA, allowing readers to try out the techniques after reading the explanations.
by Alex Smola, S.V.N. Vishwanathan - Cambridge University Press
Over the past two decades Machine Learning has become one of the mainstays of information technology and a rather central part of our life. Smart data analysis will become even more pervasive as a necessary ingredient for technological progress.
by Mark Watson - Lulu.com
The book uses the author's libraries and the best of open source software to introduce AI (Artificial Intelligence) technologies like neural networks, genetic algorithms, expert systems, machine learning, and NLP (natural language processing).