晒出你见过最好的”机器学习“资源！有没有优秀经管资源，是牛人和弱逼的最大区别！

积极加入！

这个好，集中优势兵力于一身

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