圖書標籤: 機器學習 MachineLearning 數據挖掘 計算機 計算機科學 概率 統計 人工智能
发表于2025-02-14
Machine Learning pdf epub mobi txt 電子書 下載 2025
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Kevin P. Murphy is Associate Professor in the Department of Computer Science and in the Department of Statistics at the University of British Columbia.
machine learning教材
評分感覺有點泛泛
評分Chapter 1-3, 07.09.2019; C4 (Gaussian models) 07.12; C5 (Bayesian statistics) 07.19;C6 (Frequentist statistics) 07.20; C7 (Linear regression) 07.29; C8 (Logistic regression) 08.22
評分很完整的推導,適閤寫代碼參考
評分machine learning教材
为什么评分这么高?谁学过,谁学完了?为什么评分这么高?谁学过,谁学完了?为什么评分这么高?谁学过,谁学完了?为什么评分这么高?谁学过,谁学完了?为什么评分这么高?谁学过,谁学完了?为什么评分这么高?谁学过,谁学完了?为什么评分这么高?谁学过,谁学完了?为什么评分这...
評分-----------------------------读完第三章更新------------------------------ 啪啪啪啪啪啪啪啪啪啪啪,先自扇十个大耳光。 这本书还是不错的,很深,我写了个第三章的笔记,欢迎拍砖。http://book.douban.com/annotation/23203104/ 第三章可读性比第二章好得多,但是说实话还...
評分Awesome! 1. 与这本书的缘分竟始于化学系图书馆(没有其它两本,PRML or the Elements,也许因为K Murphy是校友的缘故。。不过C Bishop就在附近的Microsoft啊) 最终在黑五我还是买了这本书,装帧结实漂亮;留白够多,这样可以随意增添喜欢的内容和推导。英Amazon比较厚道,便宜...
評分哥们就是一个苦逼的本科小民工啊,在ml上完全没有受到过系统的学习,从大约1年半前开始接触机器学习至今,总共看过AG的video,看过《机器学习》和《模式分类》,后来又看了李航的《统计学习方法》,啃过《prml》,学到的东西总感觉零零散散,由于远离ml的圈子,缺乏对这个领域...
評分为什么评分这么高?谁学过,谁学完了?为什么评分这么高?谁学过,谁学完了?为什么评分这么高?谁学过,谁学完了?为什么评分这么高?谁学过,谁学完了?为什么评分这么高?谁学过,谁学完了?为什么评分这么高?谁学过,谁学完了?为什么评分这么高?谁学过,谁学完了?为什么评分这...
Machine Learning pdf epub mobi txt 電子書 下載 2025