Machine Learning : A Bayesian and Optimization Perspective, 2/e (Hardcover)

Sergios Theodoridis



Machine Learning: A Bayesian and Optimization Perspective, Second Edition gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches based on optimization techniques combined with the Bayesian inference approach. The book builds from the basic classical methods to recent trends, making it suitable for different courses, including pattern recognition, statistical/adaptive signal processing, and statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. In addition, sections cover major machine learning methods developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science.

Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth and supported by examples and problems, giving an invaluable resource to both the student and researcher for understanding and applying machine learning concepts.

This updated edition includes many more simple examples on basic theory, complete rewrites of the chapter on Neural Networks and Deep Learning, and expanded treatment of Bayesian learning, including Nonparametric Bayesian Learning.


  • Presents the physical reasoning, mathematical modeling and algorithmic implementation of each method
  • Updates on the latest trends, including sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling
  • Provides case studies on a variety of topics, including protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, and more







ergios Theodoridis is Professor of Signal Processing and Machine Learning in the Department of Informatics and Telecommunications of the University of Athens.

He is the co-author of the bestselling book, Pattern Recognition, and the co-author of Introduction to Pattern Recognition: A MATLAB Approach.

He serves as Editor-in-Chief for the IEEE Transactions on Signal Processing, and he is the co-Editor in Chief with Rama Chellapa for the Academic

Press Library in Signal Processing.


He has received a number of awards including the 2014 IEEE Signal Processing Magazine Best Paper Award, the 2009 IEEE Computational Intelligence Society Transactions on Neural Networks Outstanding Paper Award, the 2014 IEEE Signal Processing Society Education Award, the EURASIP 2014 Meritorious Service Award, and he has served as a Distinguished Lecturer for the IEEE Signal Processing Society and the IEEE Circuits and Systems Society. He is a Fellow of EURASIP and a Fellow of IEEE.


埃爾吉奧斯·西奧多里迪斯(ergios Theodoridis)是雅典大學資訊學與電信學系的信號處理和機器學習教授。

他是暢銷書《模式識別》(Pattern Recognition)的合著者,也是《模式識別入門:MATLAB方法》(Introduction to Pattern Recognition: A MATLAB Approach)的合著者。

他擔任IEEE信號處理期刊(IEEE Transactions on Signal Processing)的主編,並與Rama Chellapa共同擔任學術出版社信號處理圖書館(Academic Press Library in Signal Processing)的聯合主編。

他獲得了多項獎項,包括2014年IEEE信號處理雜誌最佳論文獎、2009年IEEE計算智能學會神經網絡交易杰出論文獎、2014年IEEE信號處理學會教育獎、EURASIP 2014杰出服務獎,並曾擔任IEEE信號處理學會和IEEE電路與系統學會的傑出講師。他是EURASIP和IEEE的會士。