机器学习（一）开源资料库整理--国外全英课程

水深无声 2022-08-02 09:44 226阅读 0赞

# Open Source Machine Learning Degree    #

**Learn machine learning for free, because free is better than not-free.**

本文是整理自http://nixonite.github.io/open-source-machine-learning-degree/，授人以鱼，不如授人以渔。主要是作为机器学习的理论基础和编程开发入门的导引，可以作为通俗指导资料和免费教育资源。下面的pdf链接均是个人的百度云里的地址，保证下面速度，也作为个人备份资料，同时在整理的文档后面附有原作者的github原文，为了方便下载，这是下面所有pdf文档的下载压缩包地址：http://pan.baidu.com/s/1bn9Bl6n

## Mathematics 数学基础    ##

**Calculus 微积分**

*  **Calculus** by Gilbert Strang [pdf][]

**Proofs 证明**

*  **How to Prove It** by Daniel J. Velleman[pdf][pdf 1]   本书简介见豆瓣：http://book.douban.com/subject/2869796/

**Linear Algebra 线性代数**

*  **Linear Algebra** by Jim Hefferon[pdf ][pdf 2]网易公开课有麻省理工大学的线性代数公开课，都已翻译完整：http://v.163.com/special/opencourse/daishu.html[  
    ][pdf 2]

**More Linear Algebra 线性代数扩展**

*  **Linear Algebra Done Right** by Sheldon Axler [pdf     ][pdf 3]这本书翻译为“应该这样学线性代数”，豆瓣评分9.1，豆瓣地址：http://book.douban.com/subject/2877309/[  
    ][pdf 3]
 *  **Advanced Linear Algebra** by Steven Roman [pdf     ][pdf 4]“线性代数的高级扩展”，豆瓣地址：http://book.douban.com/subject/2864219/[  
    ][pdf 4]
 *  **Advanced Linear Algebra** by Bruce E. Shapiro [pdf ][pdf 5]“线性代数基础知识的扩展”，豆瓣没搜到，sorry[  
    ][pdf 5]

**Even More Damn Linear Algebra 更鬼扯的线性代数**

*  **A Collection of Notes on Numerical Linear Algebra** by Robert A. van de Geijn [pdf][pdf 6][  
    ][Link 1]
 *  **Numerical Linear Algebra** by Lloyd N. Trefethen, David Bau, III [Google Books  ][Google Books_]这本书是线性代数导论，豆瓣地址：http://book.douban.com/subject/3638826/[  
    ][Google Books_]

**Probability and Statistics 概率论与数理统计**

*  **Introduction to Probability** by Charles M. Grinstead and Laurie Snell[pdf     ][pdf_]概率论入门，豆瓣没找到，sorry[  
    ][pdf_]
 *  **All of Statistics** by Larry Wasserman [pdf   ][pdf 7]豆瓣评分8.9，豆瓣地址：http://book.douban.com/subject/2285151/[  
    ][pdf 7]

## Introductory Machine Learning 机器学习介绍    ##

*  **Introduction to Machine Learning** by Alex Smola and S.V.N. Vishwanathan [pdf][pdf 8]
 *  **Introduction to Machine Learning - The Wikipedia Guide** by [Nixonite][] [pdf][pdf 9]
 *  **Introduction to Machine Learning** by Ethem Alpaydin [pdf ][pdf 10]作者卡内基梅隆大学教授，豆瓣评分7.5，豆瓣地址：http://book.douban.com/subject/2373579/[  
    ][pdf 10]

## Computer Vision 计算机视觉    ##

*  **Computer Vision: Algorithms and Applications** by Richard Szeliski [pdf][pdf 11]

## Reinforcement Learning 强化学习    ##

*  **Introduction to Reinforcement Learning** by Sutton and Barto [html][]

## Probabilistic Graphical Models 概率图论模型    ##

*  **A Brief Introduction to Graphical Models and Bayesian Networks** by Kevin Murphy [pdf][pdf 12] [html][html 1]  图论模型和贝叶斯网络的简要介绍[  
    ][html 1]
 *  **An Introduction to Graphical Models** by Kevin Murphy [pdf][pdf 13]   图论模型入门[  
    ][pdf 13]
 *  **Probabilistic Graphical Models: Principles and Techniques** by Koller, Friedman [pdf][pdf 14]  概率图论模型：原理和技术[  
    ][pdf 14]
 *  **Bayesian Reasoning and Machine Learning** by David Barber [pdf][pdf 15]  贝叶斯推论和机器学习[  
    ][pdf 15]

## Applied Machine Learning  机器学习的应用    ##

*  **Natural Language Processing with Python** by Steven Bird et al. [pdf][pdf 16][ (Python 2)][_Python 2] [html (Python 3) ][html _Python 3]Python语言开发自然语言处理[  
    ][html _Python 3]
 *  **Machine Learning in Action** by Peter Harrington [pdf][pdf 17]  机器学习实战[  
    ][pdf 17]
 *  **An Introduction to Statistical Learning with Applications in R** by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani pdf

## Machine Learning - HARD MODE   机器学习-困难模式    ##

*  **Elements of Statistical Learning** by Hastie et al.[html][html 2]  统计学习基础[  
    ][html 2]
 *  **Pattern Recognition and Machine Learning** by Christopher M. Bishop [pdf][pdf 18]  模式识别与机器学习[  
    ][pdf 18]
 *  **Information Theory, Inference, and Learning Algorithms** by David J. C. MacKay [pdf][pdf 19] 信息理论、推理和学习算法

——————————————————————————————————————————————————————————————————

下面给出原文：

http://nixonite.github.io/open-source-machine-learning-degree/

# Open Source Machine Learning Degree #

Learn machine learning for free, because free is better than not-free.

This website is inspired by the [ datasciencemasters/go][datasciencemasters_go] and [ open-source-cs-degree][open-source-cs-degree] github pages. This one is specifically for machine learning and features textbooks, textbook-length lecture notes, and similar materials found with a simple google search. This repository is meant as a general guide and resource for a free education.

Note: Please report any broken links as an issue on the Github page. Thanks!

## Mathematics ##

**Calculus**

*  *Calculus* by Gilbert Strang [ pdf][pdf 20]

**Proofs**

*  *How to Prove It* by Daniel J. Velleman [ pdf][pdf 21]

**Linear Algebra**

*  *Linear Algebra* by Jim Hefferon [ pdf][pdf 22]

**More Linear Algebra**

*  *Linear Algebra Done Right* by Sheldon Axler [ pdf][pdf 23]
 *  *Advanced Linear Algebra* by Steven Roman [ pdf][pdf 24]
 *  *Advanced Linear Algebra* by Bruce E. Shapiro [ pdf][pdf 25]

**Even More Damn Linear Algebra**

*  *A Collection of Notes on Numerical Linear Algebra* by Robert A. van de Geijn[pdf (optional donation to the author on his website)][Link 1]
 *  *Numerical Linear Algebra* by Lloyd N. Trefethen, David Bau, III [ Google Books][Google Books_]

**Probability and Statistics**

*  *Introduction to Probability* by Charles M. Grinstead and Laurie Snell[pdf][pdf 26]
 *  *All of Statistics* by Larry Wasserman [ pdf][pdf 27]

## Introductory Machine Learning ##

*  *Introduction to Machine Learning* by Alex Smola and S.V.N. Vishwanathan[pdf][pdf 28]
 *  *Introduction to Machine Learning - The Wikipedia Guide* by [ Nixonite][Nixonite] [ pdf][pdf 29]
 *  *Introduction to Machine Learning* by Ethem Alpaydin [ pdf][pdf 30]

## Computer Vision ##

*  *Computer Vision: Algorithms and Applications* by Richard Szeliski [ pdf][pdf 31]

## Reinforcement Learning ##

*  *Introduction to Reinforcement Learning* by Sutton and Barto [ html][html 3]

## Probabilistic Graphical Models ##

*  *A Brief Introduction to Graphical Models and Bayesian Networks* by Kevin Murphy[pdf][pdf 32] [ html][html 4]
 *  *An Introduction to Graphical Models* by Kevin Murphy [ pdf][pdf 33]
 *  *Probabilistic Graphical Models: Principles and Techniques* by Koller, Friedman[pdf][pdf 34]
 *  *Bayesian Reasoning and Machine Learning* by David Barber [ pdf][pdf 35]

## Applied Machine Learning ##

*  *Natural Language Processing with Python* by Steven Bird et al. [ pdf (Python 2)][_Python 2] [ html (Python 3)][html _Python 3]
 *  *Machine Learning in Action* by Peter Harrington [ pdf][pdf 36]
 *  *An Introduction to Statistical Learning with Applications in R* by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani[pdf][pdf 37]

## Machine Learning - HARD MODE ##

*  *Elements of Statistical Learning* by Hastie et al. [ pdf][pdf 38]
 *  *Pattern Recognition and Machine Learning* by Christopher M. Bishop [ pdf][pdf 39]
 *  *Information Theory, Inference, and Learning Algorithms* by David J. C. MacKay[pdf][pdf 40]

#### Legal Stuff ####

If you're the original author of any of these books, and would like me to remove the links to your material, just send me an email at[programminglinguist@gmail.com][programminglinguist_gmail.com]

[pdf]: http://pan.baidu.com/s/1mgkqwWw
[pdf 1]: http://pan.baidu.com/s/1o6Ds5a6
[pdf 2]: http://pan.baidu.com/s/1bn8pnxt
[pdf 3]: http://pan.baidu.com/s/1sjJXZUP
[pdf 4]: http://pan.baidu.com/s/1qW2zaAo
[pdf 5]: http://pan.baidu.com/s/1eQsAcEm
[pdf 6]: http://pan.baidu.com/s/1qW2zaBq
[Link 1]: http://www.cs.utexas.edu/users/flame/Notes/NotesOnNLA.pdf
[Google Books_]: https://books.google.com/books?id=bj-Lu6zjWbEC&pg=PA8&source=gbs_toc_r&cad=3#v=onepage&q&f=false
[pdf_]: http://pan.baidu.com/s/1gd4dvvP
[pdf 7]: http://pan.baidu.com/s/1o6rDMX0
[pdf 8]: http://pan.baidu.com/s/1sIkEi
[Nixonite]: https://github.com/Nixonite
[pdf 9]: http://pan.baidu.com/s/1hq4Hq4k
[pdf 10]: http://pan.baidu.com/s/12R9vg
[pdf 11]: http://pan.baidu.com/s/1jGhC8mE
[html]: http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html
[pdf 12]: http://pan.baidu.com/s/1qWolUSs
[html 1]: http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html
[pdf 13]: http://pan.baidu.com/s/1bn57yhT
[pdf 14]: http://pan.baidu.com/s/1sjn3Y9Z
[pdf 15]: http://pan.baidu.com/s/1dD2L5lf
[pdf 16]: http://pan.baidu.com/s/1pJp9Uvp
[_Python 2]: http://victoria.lviv.ua/html/fl5/NaturalLanguageProcessingWithPython.pdf
[html _Python 3]: http://www.nltk.org/book/
[pdf 17]: http://pan.baidu.com/s/1hqKWoCG
[html 2]: http://statweb.stanford.edu/~tibs/ElemStatLearn/
[pdf 18]: http://pan.baidu.com/s/1bnlUDER
[pdf 19]: http://pan.baidu.com/s/1sjxDqJN
[datasciencemasters_go]: https://github.com/datasciencemasters/go
[open-source-cs-degree]: https://github.com/mvillaloboz/open-source-cs-degree
[pdf 20]: http://ocw.mit.edu/ans7870/resources/Strang/Edited/Calculus/Calculus.pdf
[pdf 21]: https://opeconomica.files.wordpress.com/2014/08/daniel-j-velleman-how-to-prove-it.pdf
[pdf 22]: http://joshua.smcvt.edu/linearalgebra/book.pdf
[pdf 23]: http://fetweb.ju.edu.jo/staff/EE/jrahhal/PDF/sc%20-%20Linear%20Algebra%20Done%20Right.pdf
[pdf 24]: http://ksu.edu.sa/sites/py/ar/mpy/departments/math/learnResources/ResourceCenter/Documents/Advanced%20Linear%20Algebra%20-%20Steven%20Roman.pdf
[pdf 25]: http://beshapiro.com/math462/462notes.pdf
[pdf 26]: https://www.dartmouth.edu/%7Echance/teaching_aids/books_articles/probability_book/amsbook.mac.pdf
[pdf 27]: http://www.ic.unicamp.br/%7Ewainer/cursos/1s2013/ml/livro.pdf
[pdf 28]: http://alex.smola.org/drafts/thebook.pdf
[pdf 29]: https://github.com/Nixonite/open-source-machine-learning-degree/raw/master/Introduction%20to%20Machine%20Learning%20-%20Wikipedia.pdf
[pdf 30]: http://stp.lingfil.uu.se/%7Esantinim/ml/2014/Alpaydin2010_IntroductionToMl_2ed.pdf
[pdf 31]: http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf
[html 3]: http://webdocs.cs.ualberta.ca/%7Esutton/book/the-book.html
[pdf 32]: http://www.cs.ubc.ca/%7Emurphyk/Bayes/bayes_tutorial.pdf
[html 4]: http://www.cs.ubc.ca/%7Emurphyk/Bayes/bnintro.html
[pdf 33]: http://www.cs.ubc.ca/%7Emurphyk/Papers/intro_gm.pdf
[pdf 34]: http://vk.com/doc168073_304660839?hash=39a33dd8aa6b141d8a&dl=b667454bc650f66cc0
[pdf 35]: http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf
[pdf 36]: http://www2.ift.ulaval.ca/%7Echaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf
[pdf 37]: http://www-bcf.usc.edu/%7Egareth/ISL/ISLR%20Fourth%20Printing.pdf
[pdf 38]: http://statweb.stanford.edu/%7Etibs/ElemStatLearn/
[pdf 39]: http://www.rmki.kfki.hu/%7Ebanmi/elte/Bishop%20-%20Pattern%20Recognition%20and%20Machine%20Learning.pdf
[pdf 40]: http://www.inference.phy.cam.ac.uk/itprnn/book.pdf
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