Machine Learning

Zhi-Hua Zhou (Author) Shaowu Liu (Translator)

Machine Learning
  • 出版商: Springer
  • 出版日期: 2021-08-21
  • 售價: $2,650
  • 貴賓價: 9.5 折 $2,518
  • 語言: 英文
  • 頁數: 464
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 9811519668
  • ISBN-13: 9789811519666
  • 相關分類: Machine Learning
  • 此書翻譯自: 機器學習

    • 立即出貨 (庫存=1)

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商品描述

Machine Learning, a vital and core area of artificial intelligence (AI), is propelling the AI field ever further and making it one of the most compelling areas of computer science research. This textbook offers a comprehensive and unbiased introduction to almost all aspects of machine learning, from the fundamentals to advanced topics. It consists of 16 chapters divided into three parts: Part 1 (Chapters 1-3) introduces the fundamentals of machine learning, including terminology, basic principles, evaluation, and linear models; Part 2 (Chapters 4-10) presents classic and commonly used machine learning methods, such as decision trees, neural networks, support vector machines, Bayesian classifiers, ensemble methods, clustering, dimension reduction and metric learning; Part 3 (Chapters 11-16) introduces some advanced topics, covering feature selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, and reinforcement learning. Each chapter includes exercises and further reading, so that readers can explore areas of interest.

The book can be used as an undergraduate or postgraduate textbook for computer science, computer engineering, electrical engineering, data science, and related majors. It is also a useful reference resource for researchers and practitioners of machine learning.

商品描述(中文翻譯)

機器學習是人工智慧（AI）的一個重要核心領域，推動著AI領域不斷向前發展，使其成為計算機科學研究中最具吸引力的領域之一。本教科書提供了對機器學習幾乎所有方面的全面且客觀的介紹，涵蓋從基礎到進階主題。全書共分為16章，分為三個部分：第一部分（第1-3章）介紹機器學習的基本概念，包括術語、基本原則、評估和線性模型；第二部分（第4-10章）介紹經典且常用的機器學習方法，如決策樹、神經網絡、支持向量機、貝葉斯分類器、集成方法、聚類、降維和度量學習；第三部分（第11-16章）介紹一些進階主題，涵蓋特徵選擇和稀疏學習、計算學習理論、半監督學習、概率圖模型、規則學習和強化學習。每章都包含練習題和進一步閱讀的建議，讓讀者可以探索感興趣的領域。

本書可作為計算機科學、計算機工程、電機工程、數據科學及相關專業的本科或研究生教材。對於機器學習的研究者和實踐者來說，它也是一個有用的參考資源。

作者簡介

Zhi-Hua Zhou is a leading expert on machine learning and artificial intelligence. He is currently a Professor, Head of the Department of Computer Science and Technology, Dean of the School of Artificial Intelligence, and the founding director of the LAMDA Group at Nanjing University, China. Prof. Zhou has authored the books "Ensemble Methods: Foundations and Algorithms" (2012) and "Machine Learning" (in Chinese, 2016), and published more than 200 papers in top-tier international journals and conferences. He founded the ACML (Asian Conference on Machine Learning), and served as chairperson for many prestigious conferences, including AAAI 2019 program chair, ICDM 2016 general chair, IJCAI 2015 machine learning track chair, and area chair for NeurIPS, ICML, AAAI, IJCAI, KDD, etc. He is editor-in-chief of Frontiers of Computer Science, and has been an associate editor for prestigious journals such as the Machine Learning journal and IEEE PAMI. He is a Fellow of the ACM, AAAI, AAAS, IEEE, IAPR, IET/IEE, CCF and CAAI, and recipient of numerous awards, including the National Natural Science Award of China and the IEEE Computer Society Edward J. McCluskey Technical Achievement Award.

 

作者簡介(中文翻譯)

Zhi-Hua Zhou 是機器學習和人工智慧領域的領先專家。他目前擔任中國南京大學計算機科學與技術系教授、系主任、人工智慧學院院長，以及 LAMDA 團隊的創始主任。Zhou 教授著有《集成方法：基礎與演算法》（2012）和《機器學習》（中文，2016）等書籍，並在頂尖國際期刊和會議上發表了超過 200 篇論文。他創辦了 ACML（亞洲機器學習會議），並擔任多個知名會議的主席，包括 AAAI 2019 程序主席、ICDM 2016 總主席、IJCAI 2015 機器學習專題主席，以及 NeurIPS、ICML、AAAI、IJCAI、KDD 等會議的領域主席。他是《計算機科學前沿》的主編，並曾擔任《機器學習期刊》和 IEEE PAMI 等知名期刊的副編輯。他是 ACM、AAAI、AAAS、IEEE、IAPR、IET/IEE、CCF 和 CAAI 的會士，並獲得多項獎項，包括中國國家自然科學獎和 IEEE 計算機學會 Edward J. McCluskey 技術成就獎。

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