Introduction to Statistical Machine Learning(美國原版)

Masashi Sugiyama

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商品描述

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing 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 provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks.

  • Provides the necessary background material to understand machine learning such as statistics, probability, linear algebra, and calculus.
  • Complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning.
  • Includes MATLAB/Octave programs 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.

商品描述(中文翻譯)

機器學習使得電腦能夠在沒有實際編程的情況下學習和辨識模式。當統計技術和機器學習結合在一起時,它們成為一種強大的工具,用於分析各種類型的數據,包括圖像處理、語音處理、自然語言處理、機器人控制,以及生物學、醫學、天文學、物理學和材料等基礎科學領域。

《統計機器學習入門》提供了一個總體介紹機器學習的書籍,簡潔地涵蓋了各種主題,幫助您填補理論和實踐之間的差距。第一部分討論了統計學和概率論的基本概念,這些概念在描述機器學習算法時使用。第二部分和第三部分解釋了機器學習技術的兩種主要方法:生成方法和判別方法。第三部分深入探討了在實踐中使機器學習算法更有用的高級主題。配套的MATLAB/Octave程序提供了完成各種數據分析任務所需的實踐技能。

本書提供了理解機器學習所需的背景材料,如統計學、概率論、線性代數和微積分。全面介紹了統計模式識別的生成方法和統計機器學習的判別方法。包括MATLAB/Octave程序,讓讀者能夠數值地測試算法,並在各種數據分析任務中獲得數學和實踐技能。討論了機器學習和統計學的各種應用,並提供了從圖像處理、語音處理、自然語言處理、機器人控制,到生物學、醫學、天文學、物理學和材料等領域的實例。