Machine learning allows computers to learn and discern patterns without being programmed. When Statistical techniques and machine learning are combined together, they are a powerful tool for analyzing 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, Second Edition provides a general introduction to machine learning that covers a wide range of topics concisely and will help readers bridge the gap between theory and practice. Parts 1 and 2 discuss the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part 3 and Part 4 explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Parts 5 and 6 provide an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice, including creating full-fledged algorithms in a range of real-world applications drawn from research areas such as image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials. The algorithms developed in the book include Python program code to provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks. The Second Edition also includes an all-new Part 6 on on Deep Learning, including chapters on Feedforward Neural Networks, Neural Networks with Image Data, Neural Networks with Sequential Data, learning from limited data, Representation Learning, Deep Generative Modeling, and Multimodal Learning.

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

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

Includes Python program code 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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