Introduction to Statistical Machine Learning: Edition 2
Masashi Sugiyama · Takashi Ishida
Sep 2026 · Morgan Kaufmann
Ebook
650
Pages
About this ebook
Introduction to Statistical Machine Learning, Second Edition provides a general introduction to the fundamental concepts of statistics and probability that are used in describing machine learning algorithms, covering the two major approaches of machine learning techniques, generative methods and discriminative methods. In addition, it explores 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 readers with the necessary, practical skills needed to accomplish a wide range of data analysis tasks. The new edition also includes an all-new section 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, including statistics, probability, linear algebra, and calculus - Presents 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, biology, medicine, astronomy, physics, and materials
Introduction to Statistical Machine Learning, Second Edition provides a general introduction to the fundamental concepts of statistics and probability that are used in describing machine learning algorithms, covering the two major approaches of machine learning techniques, generative methods and ...
About the author
Masashi Sugiyama received the degrees of Bachelor of Engineering, Master of Engineering, and Doctor of Engineering in Computer Science from Tokyo Institute of Technology, Japan in 1997, 1999, and 2001, respectively. In 2001, he was appointed Assistant Professor in the same institute, and he was promoted to Associate Professor in 2003. He moved to the University of Tokyo as Professor in 2014. He received an Alexander von Humboldt Foundation Research Fellowship and researched at Fraunhofer Institute, Berlin, Germany, from 2003 to 2004. In 2006, he received a European Commission Program Erasmus Mundus Scholarship and researched at the University of Edinburgh, Edinburgh, UK. He received the Faculty Award from IBM in 2007 for his contribution to machine learning under non-stationarity, the Nagao Special Researcher Award from the Information Processing Society of Japan in 2011 and the Young Scientists' Prize from the Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology Japan for his contribution to the density-ratio paradigm of machine learning. His research interests include theories and algorithms of machine learning and data mining, and a wide range of applications such as signal processing, image processing, and robot control.Dr. Takashi Ishida is a Lecturer at Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo. He is also affiliated with Department of Computer Science, Graduate School of Information Science and Technology and Department of Information Science, Faculty of Science. Dr. Ishida received his PhD from the University of Tokyo in 2021, advised by Prof. Masashi Sugiyama. Prior to that, he received the MSc from the University of Tokyo in September 2017 and the Bachelor of Economics from Keio University in March 2013.
Masashi Sugiyama received the degrees of Bachelor of Engineering, Master of Engineering, and Doctor of Engineering in Computer Science from Tokyo Institute of Technology, Japan in 1997, 1999, and 2001, respectively. In 2001, he was appointed Assistant Professor in the same institute, and he was p...
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