希望大家多多分享！工作后学习的机会少了很多，好遗憾！

thanks

这个建议不错，可以好好学习一下了

the elements of statistical learning

Journal of Machine Learning Research
http://jmlr.org/

The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online.
JMLR has a commitment to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1533-7928) immediately upon receipt. Until the end of 2004, paper volumes (ISSN 1532-4435) were published 8 times annually and sold to libraries and individuals by the MIT Press. Paper volumes (ISSN 1532-4435) are now published and sold by Microtome Publishing.

https://www.jstatsoft.org/index
Established in 1996, the Journal of Statistical Software publishes articles, book reviews, code snippets, and software reviews on the subject of statistical software and algorithms. The contents are freely available on-line. For both articles and code snippets the source code is published along with the paper. Statistical software is the key link between statistical methods and their application in practice. Software that makes this link is the province of the journal, and may be realized as, for instance, tools for large scale computing, database technology, desktop computing, distributed systems, the World Wide Web, reproducible research, archiving and documentation, and embedded systems. We attempt to present research that demonstrates the joint evolution of computational and statistical methods and techniques. Implementations can use languages such as C, C++, S, Fortran, Java, PHP, Python and Ruby or environments such as Mathematica, MATLAB, R, S-PLUS, SAS, Stata, and XLISP-STAT.

http://stat.ethz.ch/~buhlmann/publications/

Peter Bühlmann

Home | Publications | Software | Teaching | Other Activities
Recent publications and Preprints

https://works.bepress.com/mark_van_der_laan/

About Mark J. van der Laan

Our research involves developing statistical methods and theories for the analysis of data as commonly arise in randomized controlled trials and observational studies. In particular, we are concerned with methods dealing in proper ways with informative censoring, confounding, the curse of dimensionality, multiple testing, and data adaptive selection of models. Our philosophy is targeted learning, formalized by our recent work on targeted maximum likelihood learning, and unified loss based learning.
This statistical approach aims to let the data speak for the purpose of answering a particular scientific question of interest, and provide robust tests of null hypotheses of interest. We are continuously concerned with bringing these methods into practice and benchmark them by the practical performance on simulated and real data.
Please note Web site for the new book, Targeted Learning: www.targetedlearningbook.com

资料太多     

这个活动不错

《Mining of Massive Datasets》
数据挖掘：概念与技术 Data Mining: Concepts and Techniques
Introduction to Algorithms

学堂在线上有几门课程，搜索大数据就出来，讲解的浅显易懂

非常好，谢谢分享

分享知识，收获成长

刚买了一本文本挖掘的书，国人的作品，看了一会，没有任何新意，直接扔到垃圾堆啦！

推荐一本经典好书——《Machine learning in Action》, 作者使：Peter Harrington等，非常棒的书！

这个活动不错，跑来支持下，顺便也把自己的电子资源整理下，有机会上传给大家

入门级图书《统计学习方法》，强力推荐

需要具备扎实的数学、统计、优化、计算机等多门交叉学科的理论与实践！

http://web.stanford.edu/~hastie/pub.htm
        Books
        Papers - 2016
        Papers - 2015
        Papers - 2014
        Papers - 2013
        Papers - 2012
        Papers - 2011
        Papers - 2010
        Papers - 2009
        Papers - 2008
        Papers - 2007
        Papers - 2006
        Papers - 2005
        Papers - 2004
        Papers - 2003
        Papers - 2002
        Papers - 2001
        Papers - 2000
        Papers - 1999
        Papers - 1998
        Papers - 1997
        Papers - 1996
        Papers - 1995
        Papers - 1994
        Papers - 1993
        Papers - 1992
        Papers - 1991
        Papers - 1990
        Papers - 1987
        Papers - 1986
        Thesis - 1984

要读，就读大师的原文原著，既使读懂一分，也比读其它收获大，再列两位大师级的个人网页：
http://is.tuebingen.mpg.de/person/bs
Bernhard Schölkopf
My scientific interests are in the field of machine learning and inference from empirical data. In particular, I study kernel methods for extracting regularities from possibly high-dimensional data. These regularities are usually statistical ones, however, in recent years I have also become interested in methods for finding causal structures that underly statistical dependences. I have worked on a number of different applications of machine learning - in data analysis, you get "to play in everyone's backyard." Most recently, I have been trying to play in the backyard of astronomers and photographers.

I am heading the Department of Empirical Inference; to learn more about our work, take a look at the Department Overview.

Many of my papers can downloaded if you click on the tab "publications;" alternatively, from http://www.kernel-machines.org/ or from arxiv.  Some additional links:

First chapter of our book Learning with Kernels.
Review paper on kernel methods in the Annals of Statistics.
Short high-level introduction on statistical learnig theory (in German) that appeared in the 2004 Jahrbuch of the Max Planck Society.
Obituary for Alexej Chervonenkis (NIPS 2014).
With the growing interest in (how to make money with) big data, machine learning has significantly gained in popularity. We have published an article in the German newspaper FAZ, discussing some of the implications. Disclaimer: the text that appears above our names was neither written nor approved by us.
A children's book
Photographs: view of the Alps from the southern black forest, a rainbow in La Palma, a lunar eclipse in 2007, the Andromeda galaxy, the Milky Way on the Roque de los Muchachos, the North America Nebula, the constellation Orion with Barnard's loop, and finally a picture of a beautiful northern light, which I took a few years ago from the plane, on the way home from a conference in Vancouver. I always try to get a window seat when flying home from the North American west coast - it is surprizingly common to see northern lights. Looking at the night sky is a fascinating and humbling experience.

最后，最值得纪念的：Leo Breiman，randomForest 创始人。
http://www.stat.berkeley.edu/~breiman/papers.html
513.pdf
518.pdf
adaptbag99.pdf
arc97.pdf
arcall.pdf
arcall96.pdf
arcing-the-edge.pdf
bagging.pdf
BAtrees.pdf
curds-whey-all.pdf
curds-whey-justfigs.pdf
curds-whey-justtables.pdf
curds-whey-justtext.pdf
DB-CART.pdf
games.pdf
half&half.pdf
nldiscanace.pdf
notes_on_random_forests_v2.pdf
OOBestimation.pdf
pastebite.pdf
pcart.pdf
random-forests.pdf
randomforest2001.pdf
randomforests-rev.pdf
SF_Manual.pdf
siamtalk2003.pdf
some_theory2000.pdf
some_theory2001.pdf
Using_random_forests_V3.0.pdf
Using_random_forests_v3.00.pdf
Using_random_forests_V3.1.pdf
Using_random_forests_v4.0.old.pdf
Using_random_forests_v4.0.pdf
wald2002-1.pdf
wald2002-2.pdf
wald2002-3.pdf

系统的学习方法也很重要

互相学习

中华励志网www.zhlzw.com中名著阅读，有古今中外各类经典图书在线阅读资源，比如弗里德曼《资本主义与自由》，马歇尔《经济学原理》，哈耶克《个人主义与经济秩序》，同时也有亚里士多德《形而上学》黑格尔《精神现象学》等经典名著。进入网页后搜索书名或作者即可。祝你读书愉快学有所成！

[tongue]

这个厉害

单纯关于机器学习的书籍上面基本分享完整了。这里我想分享一下与机器学习相关的数学,统计基础资源（它们不一定是其本领域最好 最受推崇的，但个人认为它们是最适合与机器学习结合学习的资源）。毕竟要想在这方面走的更远，理解更深，拥有良好数学基础是必须的。

1.线性代数

推荐书籍：《Introduction to Linear Algebra》By gilbert strang (不要弄错了，有另外一本书 名字相同，但作者不同）
        这本书国内似乎没有正版书籍卖（更别说中文版），需要的要么使用第四版电子版，要么淘宝买打印书籍，质量一般可以凑合看。虽然第五版已经出来了，但是国内连完整连电子书都找不到。
        有些人可能觉得这本书可能在内容上有点浅，但个人认为非常值得一看的，它很多内容与国内书籍讲解角度都不同
配套公开课：http://open.163.com/special/opencourse/daishu.html  （讲师就是上面书的作者）
备选书籍：《Linear Algebra and Its Applications》by David C.La  （中文名：线性代数及其应用）
          这本书内容组织上就和国内书籍比较像了，有兴趣也是值得一看的，豆瓣评价也很好


2.概率论

公开课：http://open.163.com/special/Khan/probability.html


3.统计学

推荐书籍：《all of statistics》   
       还是那么说，单纯从统计学角度这本书也许不是很好 ，因为这本书侧重基本的统计概念(全但不是很深），但它比较适合非统计出身的工科学生，它基本上包括了所有的机器学习、数据挖掘里常用模型涉及的概率或统计概念。
备选书籍：《The Elements of Statistical Learning 》 这本书评价比较高，但相对偏统计理论，且内容也比较难，个人凭自己水平选择吧。
备选书籍：《Applied Multivariate Statistical Analysi》 By 约翰逊
统计公开课：http://open.163.com/special/Khan/khstatistics.html   （非与上面教材配套）


4.微积分

公开课：http://v.163.com/special/sp/singlevariablecalculus.html
公开课：http://open.163.com/special/opencourse/weijifen.html


5.最优化 ：

推荐书籍：《Numerical_Optimization》 By Jorge Nocedal
      这本书没有下面那本《Convex Optimization》受欢迎，但从豆瓣评价来看，它更适合非统计出生的工科人员。很多人认为这本书更适合当一本工具书（字典），因此最好选择部分重要章节精度。
备选书籍：《Convex Optimization》 By Stephen Boyd
      这本书侧重与凸优化，分理论 应用 算法三个部分，豆瓣评价很高，也很受欢迎（或许是最优化方面最受欢迎的书籍了吧）。关于它与《Numerical_Optimization》比较可以参考知乎：https://www.zhihu.com/question/49689245/answer/117439776  若能力时间精力足够，当然最好两本结合看。


此外，关于机器学习中最受欢迎的一个分支——深度学习的资料

MIT书籍：《Deep learning》 By Yoshua Bengio,Ian Goodfellow,Aaron Courville  (只有电子英文版）
https://github.com/HFTrader/DeepLearningBook
斯坦福在线深度学习教程： http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial
Neural Networks and Deep Learning：http://neuralnetworksanddeeplearning.com/index.html  （免费在线书籍）

资料很多！