Introduction to Machine Learning, 4/e (Hardcover)

Alpaydin, Ethem

  • 出版商: Summit Valley Press
  • 出版日期: 2020-03-24
  • 定價: $1,490
  • 售價: 9.8$1,460
  • 語言: 英文
  • 頁數: 712
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 0262043793
  • ISBN-13: 9780262043793
  • 相關分類: Machine Learning
  • 銷售排行: 👍 2020 年度 英文書 銷售排行 第 19 名

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商品描述

A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural networks.

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Machine learning underlies such exciting new technologies as self-driving cars, speech recognition, and translation applications. This substantially revised fourth edition of a comprehensive, widely used machine learning textbook offers new coverage of recent advances in the field in both theory and practice, including developments in deep learning and neural networks.

The book covers a broad array of topics not usually included in introductory machine learning texts, including supervised learning, Bayesian decision theory, parametric methods, semiparametric methods, nonparametric methods, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing. The fourth edition offers a new chapter on deep learning that discusses training, regularizing, and structuring deep neural networks such as convolutional and generative adversarial networks; new material in the chapter on reinforcement learning that covers the use of deep networks, the policy gradient methods, and deep reinforcement learning; new material in the chapter on multilayer perceptrons on autoencoders and the word2vec network; and discussion of a popular method of dimensionality reduction, t-SNE. New appendixes offer background material on linear algebra and optimization. End-of-chapter exercises help readers to apply concepts learned. Introduction to Machine Learning can be used in courses for advanced undergraduate and graduate students and as a reference for professionals.

商品描述(中文翻譯)

一本包含深度學習和神經網絡最新進展的綜合教材的第四版經過大幅修訂。

機器學習的目標是通過使用示例數據或過去的經驗來解決特定問題。機器學習是自動駕駛汽車、語音識別和翻譯應用等令人興奮的新技術的基礎。這本廣泛使用的機器學習教材的第四版在理論和實踐方面都新增了對該領域最新進展的內容,包括深度學習和神經網絡的發展。

該書涵蓋了許多通常不包含在入門機器學習教材中的主題,包括監督學習、貝葉斯決策理論、參數方法、半參數方法、非參數方法、多變量分析、隱馬爾可夫模型、強化學習、核機器、圖形模型、貝葉斯估計和統計檢驗。第四版新增了一章關於深度學習的內容,討論了訓練、正則化和結構化深度神經網絡,如卷積神經網絡和生成對抗網絡;在強化學習章節中新增了關於使用深度網絡、策略梯度方法和深度強化學習的內容;在多層感知器章節中新增了有關自編碼器和word2vec網絡的內容;並討論了一種常用的降維方法t-SNE。新的附錄提供了線性代數和優化的背景材料。章末練習幫助讀者應用所學概念。《機器學習導論》可用於高級本科和研究生課程,也可作為專業人士的參考書。