Introduction to Statistical Machine Learning

Machine learning allows computers to learn and discern patterns without actually being programmed. When statistical techniques and machine learning are combined, they form a powerful tool for analyzing various kinds of data in many computer science and 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.

It provides a general introduction to machine learning that covers a wide range of topics concisely and helps bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability used in describing machine learning algorithms. Parts II and III explain the two major approaches of machine learning techniques: generative methods and discriminative methods. Part III offers an in-depth look at advanced topics that are essential for making machine learning algorithms more useful in practice.

The accompanying MATLAB/Octave programs equip you with the necessary practical skills 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.