Machine Learning: The Complete Guide
Publisher: Wikipedia 2014
Contents: Introduction and Main Principles; Background and Preliminaries; Knowledge discovery in Databases; Reasoning; Search Methods; Statistics; Main Learning Paradigms; Classification Tasks; Online Learning; Semi-supervised learning; Lazy learning and nearest neighbors; Decision Trees; Linear Classifiers; Statistical classification; Evaluation of Classification Models; Features Selection and Features Extraction; Clustering; etc.
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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 Jan-Willem van de Meent, et al. - arXiv.org
This text is designed to be a graduate-level introduction to probabilistic programming. It provides a thorough background for anyone wishing to use a probabilistic programming system, and introduces the techniques needed to build these systems.
by Ratnadip Adhikari, R. K. Agrawal - arXiv
This work presents a concise description of some popular time series forecasting models used in practice, with their features. We describe three important classes of time series models, viz. the stochastic, neural networks and SVM based models.
by David J. C. MacKay - Cambridge University Press
A textbook on information theory, Bayesian inference and learning algorithms, useful for undergraduates and postgraduates students, and as a reference for researchers. Essential reading for students of electrical engineering and computer science.