Machine Learning and Data Mining: Lecture Notes
by Aaron Hertzmann
Publisher: University of Toronto 2010
Number of pages: 134
Contents: Introduction to Machine Learning; Linear Regression; Nonlinear Regression; Quadratics; Basic Probability Theory; Probability Density Functions; Estimation; Classification; Gradient Descent; Cross Validation; Bayesian Methods; Monte Carlo Methods; Principal Components Analysis; Lagrange Multipliers; Clustering; Hidden Markov Models; Support Vector Machines; AdaBoost.
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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 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 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 Dimitri P. Bertsekas - Athena Scientific
The book considers large and challenging multistage decision problems, which can be solved by dynamic programming and optimal control, but their exact solution is computationally intractable. We discuss solution methods that rely on approximations.