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. The second edition of 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. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.
發表於2024-12-27
Introduction to Machine Learning, Second Edition (Adaptive Computation and Machine Learning) 2024 pdf epub mobi 電子書 下載
基本上傳統統計學習的知識點都梳理到瞭,而且有課後習題答案。當然從內容上說,很多東西會有些陳舊瞭,這本書是在CNN鹹魚翻身前寫的,但大體內容不錯,比如概率圖模型這些,都做瞭介紹。數學基礎,也沒有太拘泥。每個章節會略顯短,屬於打骨骼的書,長肉要看其他資料,通俗性上...
評分基本上傳統統計學習的知識點都梳理到瞭,而且有課後習題答案。當然從內容上說,很多東西會有些陳舊瞭,這本書是在CNN鹹魚翻身前寫的,但大體內容不錯,比如概率圖模型這些,都做瞭介紹。數學基礎,也沒有太拘泥。每個章節會略顯短,屬於打骨骼的書,長肉要看其他資料,通俗性上...
評分基本上傳統統計學習的知識點都梳理到瞭,而且有課後習題答案。當然從內容上說,很多東西會有些陳舊瞭,這本書是在CNN鹹魚翻身前寫的,但大體內容不錯,比如概率圖模型這些,都做瞭介紹。數學基礎,也沒有太拘泥。每個章節會略顯短,屬於打骨骼的書,長肉要看其他資料,通俗性上...
評分為瞭對機器學習能有係統性的知識,買瞭這本書。因為書裏各種公式占據瞭百分之七八十的比例,所以嗬嗬瞭。但是剩餘的百分之三十可以讀一讀的,特彆是需要對機器學習有個係統體係性的認識的話。這本書就一般吧。缺點就是數學公式太多瞭。
評分為瞭對機器學習能有係統性的知識,買瞭這本書。因為書裏各種公式占據瞭百分之七八十的比例,所以嗬嗬瞭。但是剩餘的百分之三十可以讀一讀的,特彆是需要對機器學習有個係統體係性的認識的話。這本書就一般吧。缺點就是數學公式太多瞭。
圖書標籤: 機器學習 MachineLearning 數據挖掘 計算機科學 MIT CS AI 大數據
比Tom Mitchell那本好多瞭。內容很新組織也很清????。排得也不錯 @2011-12-25 10:31:48
評分比Tom Mitchell那本好多瞭。內容很新組織也很清????。排得也不錯 @2011-12-25 10:31:48
評分比Tom Mitchell那本好多瞭。內容很新組織也很清????。排得也不錯 @2011-12-25 10:31:48
評分比Tom Mitchell那本好多瞭。內容很新組織也很清????。排得也不錯 @2011-12-25 10:31:48
評分真的隻是入門
Introduction to Machine Learning, Second Edition (Adaptive Computation and Machine Learning) 2024 pdf epub mobi 電子書 下載