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)

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 

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.
The problem sets are also mathematical and challenging.
 
Standford wiki for unsupervised learning


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).
 
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/ 

Top Viewed Courses

想去Google的绝对不能错过（原谅我用这么大大的logo ^^）

 
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
 
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

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
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
These contains statistical Data mining tutorials from Andrew Moore
Another tutorial
Notations are different, might have to map it properly.
 
一些数学：
 
Matrix
http://www.purplemath.com/modules/ordering.htm

Pre Calculus - don't know if this is helpful for the class
 
Another good resource is Gil Strang's excellent MIT Linear Algebra course
 
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

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

搜索引擎原理与系统架构

    本次课程讲授的内容包括搜索引擎处理的主要问题，以及搜索引擎的主要框架与模块构成。将帮助同学们熟悉搜索引擎的主要设计思路，以及在此基础上的模块划分与架构考虑。pdf

海量数据处理

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互联网行业的软件工程师修炼之道

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百度开放云

    本堂课将详细介绍百度开放云平台的特点和应用案例。

中国Web App开发者研究报告-周云鹏

    DCCI互联网数据中心分析师周云鹏对中国Web App开发者研究报告进行了分享。此次调研针对Web App生态发展情况，研究开发者对开放平台的认知、看法和期望。此外，周云鹏还对国内主要Web App应用开放平台的特征优势进行了总结。

如何基于开放平台实现营销创新-肖鹏

    阿普创新团队创始人掺掺以“平台需要内容，品牌掌握优质内容”来说明应用、平台以及企业品牌三者之间的关系。他认为目前通过百度开放平台推广具有以下优势：低成本、搜索导向产生持续效果、内容亲和度、长尾关键词带来精准用户、平台依托保障大流量。

posted on 2012-03-24 01:53  小唯THU  阅读(2808)  评论(0)  编辑  收藏  举报

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