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, recognize faces or spoken speech, 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. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book 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.<br /> <br /> After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.
發表於2024-06-03
Introduction to Machine Learning 2024 pdf epub mobi 電子書 下載
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 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...
圖書標籤: 機器學習 machine_learning 計算機科學 計算機 英文原版 統計學習 數據挖掘 智能
比起PRML來說,這本書顯得有些簡略。可以作為學習機器學習的outline,邊學習邊查找詳細的資料。
評分這本書是理論派的,也正是從這本書開始,我特彆喜歡看數學錶達式來錶達算法的核心思想。該書走馬觀花式地把人工智能相關的話題講瞭個遍,在學術派彆方麵作者也用比較中立的態度。
評分比起PRML來說,這本書顯得有些簡略。可以作為學習機器學習的outline,邊學習邊查找詳細的資料。
評分這本書是理論派的,也正是從這本書開始,我特彆喜歡看數學錶達式來錶達算法的核心思想。該書走馬觀花式地把人工智能相關的話題講瞭個遍,在學術派彆方麵作者也用比較中立的態度。
評分比起PRML來說,這本書顯得有些簡略。可以作為學習機器學習的outline,邊學習邊查找詳細的資料。
Introduction to Machine Learning 2024 pdf epub mobi 電子書 下載