Introduction to Machine Learning, 3/e (Hardcover)

Ethem Alpaydin

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商品描述

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.

Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.

商品描述(中文翻譯)

機器學習的目標是讓電腦利用範例資料或過去的經驗來解決特定問題。機器學習已經有許多成功的應用,包括分析過去的銷售資料以預測客戶行為、優化機器人行為以最少的資源完成任務,以及從生物信息學資料中提取知識。《機器學習導論》是一本涵蓋廣泛主題的綜合教材,通常在入門機器學習教材中不包括的主題都有涉及。主題包括監督學習、貝葉斯決策理論、參數、半參數和非參數方法、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。

機器學習正迅速成為計算機科學學生在畢業前必須掌握的技能。《機器學習導論》第三版反映了這一變化,增加了對初學者的支持,包括選定的習題解答和額外的範例資料集(附有線上程式碼)。其他重大變化包括討論異常檢測、感知器和支持向量機的排名算法、矩陣分解和譜方法、距離估計、新的核算法、多層感知器中的深度學習,以及貝葉斯方法的非參數方法。所有學習算法都有詳細解釋,使學生能夠輕鬆地從書中的方程式轉換為電腦程式。本書適用於高年級本科生和研究生,也對關注機器學習方法應用的專業人士具有興趣。