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

Sergios Theodoridis

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商品描述

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

商品描述(中文翻譯)

《機器學習:貝葉斯和優化觀點,第二版》提供了一個統一的機器學習觀點,涵蓋了基於優化技術結合貝葉斯推斷方法的概率和確定性方法。本書從基本的經典方法到最新的趨勢進行了構建,適用於不同的課程,包括模式識別、統計/自適應信號處理和統計/貝葉斯學習,以及關於稀疏建模、深度學習和概率圖模型的短期課程。此外,本書還涵蓋了不同學科中發展的主要機器學習方法,如統計學、統計和自適應信號處理以及計算機科學。

本書著重於數學背後的物理推理,深入解釋了各種方法和技術,並通過示例和問題提供支持,為學生和研究人員理解和應用機器學習概念提供了寶貴的資源。

本次更新的版本包括更多關於基本理論的簡單示例,對神經網絡和深度學習章節進行了完全重寫,以及對貝葉斯學習的擴展處理,包括非參數貝葉斯學習。

本書還包括許多案例研究,涵蓋了各種主題,包括蛋白質折疊預測、光學字符識別、文本作者識別、fMRI數據分析、變點檢測、高光譜圖像解混、目標定位等等。

作者簡介

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.

作者簡介(中文翻譯)

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。