• 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
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Book Description

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;

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• Amazon

• Machine Learning
• Data Analysis and Data Mining
• Algorithms and Data Structures
• Statistics, R Language and SAS Programming
• Probability, Stochastic Process, Queueing Theory, etc.

• Machine Learning and Data Mining Lecture Notes (Aaron Hertzmann)
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