Machine Learning pdf epub mobi txt 电子书下载 2024

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Kevin P·Murphy

The MIT Press

2012-9-18

1096

USD 90.00

Hardcover

Adaptive Computation and Machine Learning

9780262018029

图书标签: 机器学习 MachineLearning 数据挖掘计算机计算机科学概率统计人工智能

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发表于2024-12-23

Machine Learning epub 下载 mobi 下载 pdf 下载 txt 电子书下载 2024

Machine Learning pdf epub mobi txt 电子书下载 2024

图书描述

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.

Machine Learning 下载 mobi epub pdf txt 电子书

著者简介

Kevin P. Murphy is Associate Professor in the Department of Computer Science and in the Department of Statistics at the University of British Columbia.

图书目录

Machine Learning pdf epub mobi txt 电子书下载

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用户评价

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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

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不够系统，有点乱，小错有点多。瑕不掩瑜，仍是经典。Machine Learning就两本书，PRML和这本。

评分☆☆☆☆☆

简单求知好快乐 // 当初退课了，下一年wfh节奏稳的话可以再上再读

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经典教材

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Probabilistic ML课本，就写作业看看，错误连篇。。。

读后感

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纯搬运。来自：https://www.cs.ubc.ca/~murphyk/MLbook/errata.html 提交新的bug fix：https://docs.google.com/forms/d/e/1FAIpQLSdOXvmnvuIQn__t0xPyTErj53L-qo_RerImgKbXV4VfLDI6SQ/viewform?formkey=dEp2U2hRWXVpMU5nd05YcEJKVFNUdmc6MQ - preface: added printing hi...

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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...

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另外的两本分别是PRML和ESLII。这本书的成书时间最晚，刚出的时候特意花了90刀从亚马逊买的。先说说优点：新，全！刚说了，相对于另外两本书，由于成书时间较晚，所以涵盖了更多最近几年的hot topic，比如Dirichlet Process，在其他另外两本书中都没有提到过。更重要的，是...

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数学本来就弱，啃英文更苦难。。谢谢大家分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分享。分...

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判别模型几乎没怎么讲。。后面各种生成模型，贝叶斯网、随机场、MCMC、HMM。 ==========================================================================================================================================================

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Machine Learning pdf epub mobi txt 电子书下载 2024