# Introduction to Statistical Machine Learning [2016] [PDF]

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## Description

Title: Introduction to Statistical Machine Learning

Author: Masashi Sugiyama

Format: PDF

Hardcover: 534 pages

Publisher: Morgan Kaufmann; 1 edition (October 9, 2015)

Language: English

ISBN: 9780128021217

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials.

Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks.

Provides the necessary background material to understand machine learning such as statistics, probability, linear algebra, and calculus.

Complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning.

Includes MATLAB/Octave programs so that readers can test the algorithms numerically and acquire both mathematical and practical skills in a wide range of data analysis tasks

Discusses a wide range of applications in machine learning and statistics and provides examples drawn from image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials.

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