• Title Introduction to Machine Learning
• Author(s) Alex Smola, S.V.N. Vishwanathan
• Publisher: Self published by Alex.Smola.org
• Hardcover/Paperback N/A
• eBook PDF (234 pages, 10.3 MB)
• Language: English
• ISBN-10: N/A
• ISBN-13: N/A
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Book Description

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data.

This book is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining.

All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning.

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• Machine Learning
• Neural Networks and Deep Learning
• Artificial Intelligence
• Data Analysis and Data Mining

• Introduction to Machine Learning (Alex Smola, et al)
• Book Homepage (Videos of Lectures, Resources, etc.)

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