Introduction to Machine Learning
by Alex Smola, S.V.N. Vishwanathan
Publisher: Cambridge University Press 2008
Number of pages: 234
Over the past two decades Machine Learning has become one of the mainstays of information technology and with that, a rather central, albeit usually hidden, part of our life. Smart data analysis will become even more pervasive as a necessary ingredient for technological progress.
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by Zdravko Markov - Central Connecticut State University
Contents: Introduction; Concept learning; Languages for learning; Version space learning; Induction of Decision trees; Covering strategies; Searching the generalization / specialization graph; Inductive Logic Progrogramming; Unsupervised Learning ...
by Jonas Buchli, et al. - arXiv.org
The starting point is the formulation of of an optimal control problem and deriving the different types of solutions and algorithms from there. These lecture notes aim at supporting this unified view with a unified notation wherever possible.
by David Barber - Cambridge University Press
The book is designed for final-year undergraduate students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basics to advanced techniques within the framework of graphical models.
by G. James, D. Witten, T. Hastie, R. Tibshirani - Springer
This book provides an introduction to statistical learning methods. It contains a number of R labs with detailed explanations on how to implement the various methods in real life settings and it is a valuable resource for a practicing data scientist.