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Saturday, 4 April 2015

Machine Learning and Data Mining Lecture Notes


This book has been written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining.

It offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. It will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.

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;

Title Machine Learning and Data Mining Lecture Notes
Author(s) Aaron Hertzmann
Publisher: University of Toronto (February 6, 2012)
Hardcover N/A
eBook PDF (134 pages, 1.6 MB)
Language: English
ISBN-10: N/A
ISBN-13: N/A
Download: http://www.dgp.toronto.edu/~hertzman/411notes.pdf

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