网络公开课资源 ——关注CS/AI/Math

当当当当~请看这个网址 - http://www.class-central.com/ - 它是一个列表，列出几大在线课程网站（有英文字幕和习题就是好啊^^）的课程表 (比网易云课堂更原汁原味哦，现在也可以看课程图谱，学累了可以轻松几分钟，还有浙大的计算机中的数学)

Stanford's Coursera - http://www.coursera.org/
Edx - https://www.edx.org
Udacity（也是Stanford教授参与创建的）- http://www.udacity.com/
Caltech(California Institute of Technology) - http://work.caltech.edu/telecourse.htmlMachine Learning course, April 3rd till May 31st 2012

这些都是新课，在网上正在上的课。之前的MIT OCW(数学课很厉害，CS在这里)是已经结束了的课，有Multimedia content标志的课值得一听。

这些新课好多都是CS的:

最近刚结束的有Introduction to AI , Introduction to Databases(SQL,OLAP,NoSQL) and Introduction to Machine Learning

正on live的有Probabilistic Graphical Models, Natural Language Processing, Design and Analysis of Algorithms I,CS 101: Building a Search Engine

即将开始的有Introduction to Machine Learning, Learning from Data ( Introductory Machine Learning course),Computer Vision, CS212 - The Design of Computer Programs,

Stanford engineering everywhere - http://see.stanford.edu/see/courses.aspx

artificial intelligence|natural language processing
http://see.stanford.edu/see/lecturelist.aspx?coll=63480b48-8819-4efd-8412-263f1a472f5a 翻译
artificial intelligence|machine learning
http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1 网易翻译版

Some mathematical details and derivations have been omitted in this course, since this is CS229a - Applied Machine Learning at Stanford. The course with complete Mathematical Depth ( but lesser emphasis on practical application ) is CS229 - Machine Learning. In case you are interested in more algorithms, reinforcement learning and the mathematical derivation for some of the methods, you might find it interesting and useful to take a look at the regular CS229 notes.

http://cs229.stanford.edu/materials.html

The problem sets are also mathematical and challenging.

Standford wiki for unsupervised learning

http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial

Harvard university extension school -http://www.extension.harvard.edu/courses/subject/computer-science

http://www.extension.harvard.edu/open-learning-initiative/math-sets-probability

Machine Learning -Spring 2011

Carnegie Mellon University，大名鼎鼎的 Tom Mitchell

http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml

with videos, assignments, exams and solutions (also slides, exercises and exams available for the previous 9 installments of the course).

http://www.cs.cmu.edu/~avrim/ML09/index.html

It's <machine learning theory>, focusing on theoretical aspects of machine learning, I think it may consider as a advanced theory foundation to machine learning course.

Google Code University

https://code.google.com/edu/

Python Course(有个Python学习社区)
C++ Course
Curriculum Search

Top Viewed Courses

Google's Python Class
Introduction to Distributed System Design
Introduction to Parallel Programming and MapReduce

想去Google的绝对不能错过（原谅我用这么大大的logo ^^）

Programming Languages
- Google's Python Class | Understanding Python 国内下载地址翻译
- Google's C++ Class
Algorithms
- Algorithms -Princeton
- Analysis of Algorithms -Standford
- Three Beautiful Quicksorts
Distributed Systems
- Introduction to Distributed System Design
- Introduction to Parallel Programming and MapReduce
Tools 101
- Introduction to Databases and MySQL
- Linux
  - Basic Linux Commands
  - Linux Ownership and Permissions
  - Text Processing with Grep
  - Greppin' in the GNU World Lab

Some of the advanced machine learning related presentations can be found at videolectures.net http://videolectures.net/Top/Computer_Science/Machine_Learning/

Machine Learning Summer School 2009, organized by Cambridge, I found a lot of great ML scientists here given lectures, such as Christopher Bishop, David Blei, and Michael I. Jordan. Unfortunately my network is a little bad and cannot download from videolectures

http://videolectures.net/mlss09uk_cambridge

Lectures from Machine Learning Summer School 2011 - Bordeaux
http://videolectures.net/mlss2011_bordeaux/

p.s. Checked the archives, there are some resources from videolectures.net already, but it looks mlss2011_bordeaux hasn't been posted yet.

更多的好网站可以看quora上的这个各抒己见

http://www.quora.com/Machine-Learning/What-are-some-good-resources-for-learning-about-machine-learning-Why

其他一些课程合辑：

http://www.m-e-e-t.com/course/show_subject/24

http://www.douban.com/group/opencourse/

http://www.kaifangke.com/forum.php?mod=forumdisplay&fid=130

IntelligentTrading blog

http://intelligenttradingtech.blogspot.com

for those interested in applications of machine learning to trading! Mostly practical examples for the laymen(非专业人员), pretty well explained.

Some other basic course materials

http://www.autonlab.org/tutorials/

These contains statistical Data mining tutorials from Andrew Moore
Another tutorial

http://www.willamette.edu/~gorr/classes/cs449/intro.html

Notations are different, might have to map it properly.

一些数学：

Matrix

http://www.sosmath.com/matrix/matrix.html

Advance algebra

http://www.purplemath.com/modules/ordering.htm

Pre Calculus - don't know if this is helpful for the class

http://www.youtube.com/watch?v=CtRAHmeWSC0&feature=relmfu

Another good resource is Gil Strang's excellent MIT Linear Algebra course

http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010

An interesting (and funny) lecture discussing limitations of the neural networks:http://www.youtube.com/user/GoogleTechTalks#p/search/0/AyzOUbkUf3M

Scientific books and papers on AI. Interesting, but advanced:http://www.intechopen.com/subject/compu

ter-and-information-science/artificial-intelligence/

The Matrix and Quaternions FAQ
http://www.flipcode.com/documents/matrfaq.html

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