Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/murufeng/awesome-machine-learning
A curated list of awesome machine Learning tutorials,courses and communities.
https://github.com/murufeng/awesome-machine-learning
List: awesome-machine-learning
machine-learning machine-learning-algorithms machine-learning-coursera machine-learning-models machine-learning-tutorials
Last synced: 29 days ago
JSON representation
A curated list of awesome machine Learning tutorials,courses and communities.
- Host: GitHub
- URL: https://github.com/murufeng/awesome-machine-learning
- Owner: murufeng
- Created: 2019-07-24T12:51:16.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-06-16T14:46:19.000Z (over 4 years ago)
- Last Synced: 2024-10-11T00:00:46.944Z (about 1 month ago)
- Topics: machine-learning, machine-learning-algorithms, machine-learning-coursera, machine-learning-models, machine-learning-tutorials
- Homepage: https://github.com/murufeng/awesome-machine-learning
- Size: 64.5 KB
- Stars: 38
- Watchers: 2
- Forks: 11
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Awesome Machine Learning
## 目录
### Claassic Machine Learning Courses
1. Courses on machine learning
http://homepages.inf.ed.ac.uk/rbf/IAPR/researchers/MLPAGES/mlcourses.htm
2. CSC2535 – Spring 2013 Advanced Machine Learning
instructor: by Hinton, University of Toronto
homepage: http://www.cs.toronto.edu/~hinton/csc2535/
3. Stanford CME 323: Distributed Algorithms and Optimization
http://stanford.edu/~rezab/dao/
4. University at Buffalo CSE574: Machine Learning and Probabilistic Graphical Models Course
http://www.cedar.buffalo.edu/~srihari/CSE574/
5. Stanford CS229: Machine Learning spring 2019
instructor: Andrew Ng
homepage: http://cs229.stanford.edu/
Syllabus: http://cs229.stanford.edu/syllabus-spring2019.html
6. CS229T/STATS231: Statistical Learning Theory Stanford / Autumn 2018-2019
instructor: Percy Liang
homepage: http://web.stanford.edu/class/cs229t/
lecture notes: http://web.stanford.edu/class/cs229t/notes.pdf
7. CMU Fall 2015 10-715: Advanced Introduction to Machine Learning
instructor: Alex Smola, Barnabas Poczos
homepage: http://www.cs.cmu.edu/~bapoczos/Classes/ML10715_2015Fall/
video: http://pan.baidu.com/s/1qWvcsUS
8. 2015 Machine Learning Summer School: Convex Optimization Short Course
instructor: S. Boyd and S. Diamond
Lecture slides and IPython notebooks: https://stanford.edu/~boyd/papers/cvx_short_course.html
9. STA 4273H (Winter 2015): Large Scale Machine Learning
http://www.cs.toronto.edu/~rsalakhu/STA4273_2015/
10. STA 414/2104 (Fall 2015): Statistical Methods for Machine Learning and Data Mining
http://www.cs.toronto.edu/~rsalakhu/STA414_2015/
11. CSC 411 (Fall 2015): Introduction to Machine Learning
http://www.cs.toronto.edu/~rsalakhu/CSC411/
12. University of Oxford: Machine Learning: 2014-2015
homepage: https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/
course materials: https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/
lectures: http://pan.baidu.com/s/1bndbxJh#path=%252FDeep%2520Learning%2520Lectures
github: https://github.com/oxford-cs-ml-2015/
13. Computer Science 294: Practical Machine Learning (Fall 2009)
instructor: Michael Jordan
homepage: https://www.cs.berkeley.edu/~jordan/courses/294-fall09/
14. CS 281A / Stat 241A Statistical Learning Theory Spring 2014
instructor: Michael Jordan
https://people.eecs.berkeley.edu/~jordan/courses/281A-spring14/
15. Statistics, Probability and Machine Learning Short Course
homepage: http://www-staff.it.uts.edu.au/~ydxu/statistics.htm
youku: http://i.youku.com/u/UMzIzNDgxNTg5Ng
youbube: https://www.youtube.com/playlist?list=PLFze15KrfxbF0n1zTNoFIaDpxnSyfgNgc
16. Statistical Learning
https://lagunita.stanford.edu/courses/HumanitiesScience/StatLearning/Winter2014/about
16. Machine learning courses online
http://fastml.com/machine-learning-courses-online/
17. Build Intelligent Applications: Master machine learning fundamentals in five hands-on courses (Coursera)
https://www.coursera.org/specializations/machine-learning
18. Machine Learning
http://www.cs.ubc.ca/~nando/540-2013/lectures.html
19. Princeton Computer Science 598D: Overcoming Intractability in Machine Learning
http://www.cs.princeton.edu/courses/archive/spring15/cos598D/
20. Computer Science 522 Advanced Complexity Theory Spring 2014
instructor: Sanjeev Arora
http://www.cs.princeton.edu/courses/archive/spr14/cos522/
21. Princeton Computer Science 511: Theoretical Machine Learning
instructor: Rob Schapire
homepage: http://www.cs.princeton.edu/courses/archive/spring14/cos511/schedule.html
22. MACHINE LEARNING FOR MUSICIANS AND ARTISTS
https://www.kadenze.com/courses/machine-learning-for-musicians-and-artists/info
23. CMSC 726: Machine Learning
homepage: http://www.cbcb.umd.edu/~hcorrada/PML/index.html
24. MIT: 9.520: Statistical Learning Theory and Applications, Fall 2015
http://www.mit.edu/~9.520/fall15/
25. MIT: Statistical Learning Theory and Applications fall 2018
http://www.mit.edu/~9.520/fall18/
26. CMU: Machine Learning: 10-701/15-781, Spring 2011
instructor: Tom Mitchell
homepage: http://www.cs.cmu.edu/~tom/10701_sp11/
lectures: http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml
27. NLA 2015 course material
ipn: http://nbviewer.jupyter.org/github/Bihaqo/nla2015/blob/master/table_of_contents.ipynb
28. CS 189/289A: Introduction to Machine Learning(with videos)
homepage: http://www.cs.berkeley.edu/~jrs/189/
29. An Introduction to Statistical Machine Learning Spring 2014 (for ACM Class)
http://bcmi.sjtu.edu.cn/log/courses/ml_2014_spring_acm.html
30. CS 159: Advanced Topics in Machine Learning (Spring 2016)
intro: Online Learning, Multi-Armed Bandits, Active Learning, Human-in-the-Loop Learning, Reinforcement Learning
instructor: Yisong Yue
homepage: http://www.yisongyue.com/courses/cs159/
31. Advanced Statistical Computing (Vanderbilt University)
intro: Course covers numerical optimization, Markov Chain Monte Carlo (MCMC), Metropolis-Hastings, Gibbs sampling, estimation-maximization (EM) algorithms, data augmentation algorithms with applications for model fitting and techniques for dealing with missing data
homepage: http://stronginference.com/Bios8366/
lecture: http://stronginference.com/Bios8366/lectures.html
github: https://github.com/fonnesbeck/Bios8366
32. Stanford CS229: Machine Learning Spring 2016
instructor: John Duchi
homepage: http://cs229.stanford.edu/
materials: http://cs229.stanford.edu/materials.html
33. CS273a: Introduction to Machine Learning
homepage: http://sli.ics.uci.edu/Classes/2015W-273a
youtube: https://www.youtube.com/playlist?list=PLaXDtXvwY-oDvedS3f4HW0b4KxqpJ_imw
course notes: http://sli.ics.uci.edu/Classes-CS178-Notes/Classes-CS178-Notes
34. Machine Learning CS-433
homepage: http://mlo.epfl.ch/page-136795.html
github: https://github.com/epfml/ML_course
35. Machine Learning Introduction: A machine learning course using Python, Jupyter Notebooks, and OpenML
https://github.com/joaquinvanschoren/ML-course
### Machine Learning on Distributed System
1. Distributed Machine Learning with Apache Spark
edx: https://prod-edx-mktg-edit.edx.org/course/distributed-machine-learning-apache-uc-berkeleyx-cs120x
### PhD-level Courses (with video lectures)
1. Phd-level courses
reddit: https://www.reddit.com/r/MachineLearning/comments/51qhc8/phdlevel_courses/
2. Advanced Introduction to Machine Learning
homepage: http://www.cs.cmu.edu/~bapoczos/Classes/ML10715_2015Fall/index.html
video: https://www.youtube.com/playlist?list=PL4DwY1suLMkcu-wytRDbvBNmx57CdQ2pJ&jct=q4qVgISGxJql7TlE6eSLKa8Wwci8SA
3. STA 4273H (Winter 2015): Large Scale Machine Learning
http://www.cs.toronto.edu/~rsalakhu/STA4273_2015/
4. Statistical Learning Theory and Applications (MIT)
homepage: http://www.mit.edu/~9.520/fall15/index.html
video: https://www.youtube.com/playlist?list=PLyGKBDfnk-iDj3FBd0Avr_dLbrU8VG73O
5. (REGML 2016) Regularization Methods for Machine Learning
homepage: http://lcsl.mit.edu/courses/regml/regml2016/
video: https://www.youtube.com/playlist?list=PLbF0BXX_6CPJ20Gf_KbLFnPWjFTvvRwCO
6. Convex Optimization: Spring 2015
homepage: http://www.stat.cmu.edu/~ryantibs/convexopt-S15/
video: https://www.youtube.com/playlist?list=PLjbUi5mgii6BZBhJ9nW7eydgycyCOYeZ6
7. CMU: Probabilistic Graphical Models (10-708, Spring 2014)
instructor: Eric Xing
homepage: http://www.cs.cmu.edu/~epxing/Class/10708/
lecture: http://www.cs.cmu.edu/~epxing/Class/10708-14/lecture.html
8. Advanced Optimization and Randomized Methods
instructor: A. Smola, S. Sra
homepage: http://www.cs.cmu.edu/~suvrit/teach/index.html
10. Machine Learning for Robotics and Computer Vision
homepage: http://vision.in.tum.de/teaching/ws2013/ml_ws13
video: https://www.youtube.com/watch?v=QZmZFeZxEKI&list=PLTBdjV_4f-EIiongKlS9OKrBEp8QR47Wl
11. Statistical Machine Learning
homepage: http://www.stat.cmu.edu/~larry/=sml/
video: https://www.youtube.com/playlist?list=PLTB9VQq8WiaCBK2XrtYn5t9uuPdsNm7YE
mirror: http://pan.baidu.com/s/1eSuJ1Nc
### PhD-level Courses (without video lectures)
Probabilistic Graphical Models (10-708, Spring 2016)
http://www.cs.cmu.edu/~epxing/Class/10708-16/lecture.html
## Resources
* [《Brief History of Machine Learning》](http://www.erogol.com/brief-history-machine-learning/)
介绍:这是一篇介绍机器学习历史的文章，介绍很全面，从感知机、神经网络、决策树、SVM、Adaboost到随机森林、Deep Learning.
* [《Deep Learning in Neural Networks: An Overview》](http://www.idsia.ch/~juergen/DeepLearning15May2014.pdf)
介绍:这是瑞士人工智能实验室Jurgen Schmidhuber写的最新版本《神经网络与深度学习综述》本综述的特点是以时间排序，从1940年开始讲起，到60-80年代，80-90年代，一直讲到2000年后及最近几年的进展。涵盖了deep learning里各种tricks，引用非常全面.
* [《A Gentle Introduction to Scikit-Learn: A Python Machine Learning Library》](http://machinelearningmastery.com/a-gentle-introduction-to-scikit-learn-a-python-machine-learning-library/)
介绍:这是一份python机器学习库,如果您是一位python工程师而且想深入的学习机器学习.那么这篇文章或许能够帮助到你.
* [《Machine Learning is Fun!》](https://medium.com/code-poet/80ea3ec3c471)
介绍:如果你还不知道什么是机器学习，或则是刚刚学习感觉到很枯燥乏味。那么推荐一读。这篇文章已经被翻译成中文,如果有兴趣可以移步http://blog.jobbole.com/67616/
* [《The LION Way: Machine Learning plus Intelligent Optimization》](http://vdisk.weibo.com/s/ayG13we2vxyKl)
介绍:<机器学习与优化>这是一本机器学习的小册子, 短短300多页道尽机器学习的方方面面. 图文并茂, 生动易懂, 没有一坨坨公式的烦恼. 适合新手入门打基础, 也适合老手温故而知新. 比起MLAPP/PRML等大部头, 也许这本你更需要!具体内容推荐阅读:http://intelligent-optimization.org/LIONbook/
* [《深度学习与统计学习理论》](http://php-52cs.rhcloud.com/?cat=7)
介绍:作者是来自百度，不过他本人已经在2014年4月份申请离职了。但是这篇文章很不错如果你不知道深度学习与支持向量机/统计学习理论有什么联系？那么应该立即看看这篇文章.
* [《Data Science with R》](http://vdisk.weibo.com/s/ayG13we2vx5qg)
介绍:这是一本由雪城大学新编的第二版《数据科学入门》教材：偏实用型，浅显易懂，适合想学习R语言的同学选读。
* [《An Introduction to Statistical Learning with Applications in R》](http://www-bcf.usc.edu/~gareth/ISL/)
介绍：这是一本斯坦福统计学著名教授Trevor Hastie和Robert Tibshirani的新书，并且在2014年一月已经开课：https://class.stanford.edu/courses/HumanitiesScience/StatLearning/Winter2014/about
* [Best Machine Learning Resources for Getting Started](http://machinelearningmastery.com/best-machine-learning-resources-for-getting-started/)
介绍：机器学习最佳入门学习资料汇总是专为机器学习初学者推荐的优质学习资源，帮助初学者快速入门。而且这篇文章的介绍已经被翻译成[中文版](http://article.yeeyan.org/view/22139/410514)。如果你不怎么熟悉，那么我建议你先看一看中文的介绍。
* [My deep learning reading list](http://blog.sina.com.cn/s/blog_bda0d2f10101fpp4.html)
介绍:主要是顺着Bengio的PAMI review的文章找出来的。包括几本综述文章，将近100篇论文，各位山头们的Presentation。全部都可以在google上找到。
* [Cross-Language Information Retrieval](http://www.morganclaypool.com/doi/abs/10.2200/S00266ED1V01Y201005HLT008?journalCode=hlt)
介绍：这是一本书籍，主要介绍的是跨语言信息检索方面的知识。理论很多
* [探索推荐引擎内部的秘密，第 1 部分: 推荐引擎初探](http://www.ibm.com/developerworks/cn/web/1103_zhaoct_recommstudy1/index.html?ca=drs-)
介绍:本文共有三个系列，作者是来自IBM的工程师。它主要介绍了推荐引擎相关算法，并帮助读者高效的实现这些算法。 [探索推荐引擎内部的秘密，第 2 部分: 深度推荐引擎相关算法 - 协同过滤](http://www.ibm.com/developerworks/cn/web/1103_zhaoct_recommstudy2/index.html?ca=drs-),[探索推荐引擎内部的秘密，第 3 部分: 深度推荐引擎相关算法 - 聚类](http://www.ibm.com/developerworks/cn/web/1103_zhaoct_recommstudy3/index.html?ca=drs-)
* [《Advice for students of machine learning》](http://mimno.infosci.cornell.edu/b/articles/ml-learn/)
介绍：康奈尔大学信息科学系助理教授David Mimno写的《对机器学习初学者的一点建议》， 写的挺实际，强调实践与理论结合，最后还引用了冯 • 诺依曼的名言: "Young man, in mathematics you don't understand things. You just get used to them."
* [分布式并行处理的数据](http://web.stanford.edu/group/pdplab/pdphandbook/)
介绍：这是一本关于分布式并行处理的数据《Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises》,作者是斯坦福的James L. McClelland。着重介绍了各种神级网络算法的分布式实现,做Distributed Deep Learning 的童鞋可以参考下
* [《机器学习经典书籍》](http://suanfazu.com/discussion/109/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%BB%8F%E5%85%B8%E4%B9%A6%E7%B1%8D)
介绍：总结了机器学习的经典书籍，包括数学基础和算法理论的书籍，可做为入门参考书单。
* [《16 Free eBooks On Machine Learning》](http://efytimes.com/e1/fullnews.asp?edid=121516)
介绍:16本机器学习的电子书，可以下载下来在pad，手机上面任意时刻去阅读。不多我建议你看完一本再下载一本。
* [《A Large set of Machine Learning Resources for Beginners to Mavens》](http://www.erogol.com/large-set-machine-learning-resources-beginners-mavens/)
介绍:标题很大，从新手到专家。不过看完上面所有资料。肯定是专家了
* [《机器学习最佳入门学习资料汇总》](http://article.yeeyan.org/view/22139/410514)
介绍：入门的书真的很多，而且我已经帮你找齐了。
* [《机器学习&数据挖掘笔记_16（常见面试之机器学习算法思想简单梳理）》](http://www.cnblogs.com/tornadomeet/p/3395593.html)
介绍:常见面试之机器学习算法思想简单梳理,此外作者还有一些其他的[机器学习与数据挖掘文章](http://www.cnblogs.com/tornadomeet/tag/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/)和[深度学习文章](http://www.cnblogs.com/tornadomeet/tag/Deep%E3%80%80Learning/),不仅是理论还有源码。
* [《文本与数据挖掘视频汇总》](http://www.kdnuggets.com/2014/09/most-viewed-web-mining-lectures-videolectures.html)
介绍：Videolectures上最受欢迎的25个文本与数据挖掘视频汇总
* [《机器学习常见算法分类汇总》](http://www.ctocio.com/hotnews/15919.html)
介绍: 机器学习无疑是当前数据分析领域的一个热点内容。很多人在平时的工作中都或多或少会用到机器学习的算法。本文为您总结一下常见的机器学习算法，以供您在工作和学习中参考.
#### 时间序列预测
* [资源列表合集](https://github.com/murufeng/awesome-machine-learning/tree/master/Time_series)