An Introduction to Machine Learning 3/e

This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms. This Third Edition covers newer approaches that have become highly topical, including deep learning, and auto-encoding, introductory information about temporal learning and hidden Markov models, and a much more detailed treatment of reinforcement learning. The book is written in an easy-to-understand manner with many examples and pictures, and with a lot of practical advice and discussions of simple applications.

The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, rule-induction programs, artificial neural networks, support vector machines, boosting algorithms, unsupervised learning (including Kohonen networks and auto-encoding), deep learning, reinforcement learning, temporal learning (including long short-term memory), hidden Markov models, and the genetic algorithm. Special attention is devoted to performance evaluation, statistical assessment, and to many practical issues ranging from feature selection and feature construction to bias, context, multi-label domains, and the problem of imbalanced classes.

本教科書提供了對機器學習技術和算法的全面介紹。第三版涵蓋了一些最新且高度熱門的方法，包括深度學習和自編碼，以及關於時間學習和隱藏馬可夫模型的入門資訊，並對強化學習進行了更詳細的探討。本書以易於理解的方式撰寫，並提供了許多示例和圖片，以及大量的實用建議和對簡單應用的討論。

主要主題包括貝葉斯分類器、最近鄰分類器、線性和多項式分類器、決策樹、規則歸納程序、人工神經網絡、支持向量機、增強算法、無監督學習（包括Kohonen網絡和自編碼）、深度學習、強化學習、時間學習（包括長短期記憶）、隱藏馬可夫模型和遺傳算法。特別關注性能評估、統計評估以及許多實際問題，從特徵選擇和特徵構建到偏差、上下文、多標籤領域和不平衡類別問題。

Miroslav Kubat, Associate Professor at the University of Miami, has been teaching and studying machine learning for over 25 years. He has published more than 100 peer-reviewed papers, co-edited two books, served on the program committees of over 60 conferences and workshops, and is an editorial board member of three scientific journals. He is widely credited with co-pioneering research in two major branches of the discipline: induction of time-varying concepts and learning from imbalanced training sets. He also contributed to research in induction from multi-label examples, induction of hierarchically organized classes, genetic algorithms, and initialization of neural networks. Professor Kubat is also known for his many practical applications of machine learning, ranging from oil-spill detection in radar images to text categorization to tumor segmentation in MR images.

Miroslav Kubat，邁阿密大學的副教授，已經教授和研究機器學習超過25年。他發表了100多篇同行評審的論文，共同編輯了兩本書，並擔任了60多個會議和研討會的程序委員會成員，並且是三個科學期刊的編輯委員會成員。他被廣泛認為是該領域兩個主要分支的共同先驅研究者：時間變化概念的歸納和不平衡訓練集的學習。他還貢獻於多標籤示例的歸納、分層組織類的歸納、遺傳算法和神經網絡的初始化等領域的研究。Kubat教授還以他在機器學習的許多實際應用而聞名，範圍從雷達圖像中的漏油檢測到文本分類，再到MR圖像中的腫瘤分割。