Machine Learning: A Bayesian and Optimization Perspective (Hardcover)

Sergios Theodoridis

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商品描述

This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques – together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been 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, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts.

The book builds carefully from the basic classical methods  to  the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for  different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models.

  • All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods.
  • The latest trends: 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.
  • Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied.
  • MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.

商品描述(中文翻譯)

這本教學書籍提供了機器學習的整體觀點,涵蓋了基於優化技術的機率和確定性方法,以及貝葉斯推論方法,其核心在於使用層次化的機率模型。本書介紹了主要的機器學習方法,這些方法在統計學、統計和自適應信號處理以及計算機科學等不同學科中得到了發展。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了寶貴的資源,幫助學生和研究人員理解和應用機器學習概念。

本書從基本的傳統方法逐步建立到最新的趨勢,每章都盡可能地獨立,使得本書適用於不同的課程:模式識別、統計/自適應信號處理、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。

主要的傳統技術包括:均值/最小二乘回歸和濾波、卡爾曼濾波、隨機逼近和在線學習、貝葉斯分類、決策樹、邏輯回歸和提升方法。

最新的趨勢包括:稀疏性、凸分析和優化、在線分佈式算法、在RKH空間中的學習、貝葉斯推論、圖形和隱馬爾可夫模型、粒子濾波、深度學習、字典學習和潛變量建模。

案例研究包括: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、信道均衡和回聲消除,展示了理論如何應用。

所有主要算法的MATLAB代碼都可以在附帶的網站上找到,讀者可以通過該代碼進行實驗。