网络公开课资源

网络公开课资源网络公开课资源——关注CS/AI/Math当当当当~请看这个网址- / - 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 - /Edx - https://Udacity（也是Stanford教授参与创建的）-/Caltech(California Institute of Technology) -/telecourse.html Machine 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 -/see/courses.aspxartificial intelligence|natural language processing/see/lecturelist.aspx?coll=63480b48-8819-4efd-8412-263f1a472f5a 翻译artificial intelligence|machine learning/see/courseinfo.aspx?coll=348ca38a-3a6d -4052-937d-cb017338d7b1 网易翻译版Some mathematical details and derivations have been omitted inthis 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./materials.htmlThe problem sets are also mathematical and challenging.Standford wiki for unsupervised learning/wiki/index.php/UFLDL_TutorialHarvard university extension school -/courses/subject/computer-scie nce/open-learning-initiative/math-sets-probabilityMachine Learning -Spring 2011Carnegie Mellon University，大名鼎鼎的Tom Mitchell/~tom/10701_sp11/lectures.shtmlwith videos, assignments, exams and solutions (also slides, exercises and exams available for the previous 9 installments of the course)./~avrim/ML09/index.htmlIt'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 Universityhttps:///edu/Python Course(有个Python学习社区)C++ CourseCurriculum SearchTop Viewed CoursesGoogle's Python ClassIntroduction to Distributed System DesignIntroduction 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 LanguagesGoogle's Python Class | Understanding Python 国内下载地址翻译Google's C++ ClassAlgorithmsAlgorithms -PrincetonAnalysis of Algorithms -StandfordThree Beautiful QuicksortsDistributed Systems Introduction to Distributed System Design Introduction to Parallel Programming and MapReduceTools 101Introduction to Databases and MySQLLinuxBasic Linux CommandsLinux Ownership and PermissionsText Processing with GrepGreppin' in the GNU World Lab Some of the advanced machine learning related presentations can be found at /Top/Computer_Science/Machine_Learni ng/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/mlss09uk_cambridgeLectures from Machine Learning Summer School 2011 - Bordeaux /mlss2011_bordeaux/p.s. Checked the archives, there are some resources from 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/Machine-Learning/What-are-some-good-resources-for-learning-about-machine-learning-Why其他一些课程合辑：/course/show_subject/24/group/opencourse//forum.php?mod=forumdisplay&amp ;fid=130IntelligentTrading blogfor those interested in applications of machine learning totrading! Mostly practical examples for the laymen(非专业人员), pretty well explained.Some other basic course materials/tutorials/These contains statistical Data mining tutorials from Andrew Moore Another tutorial/~gorr/classes/cs449/intro.html Notations are different, might have to map it properly.一些数学：Matrix/matrix/matrix.html Advance algebra /modules/ordering.htm Pre Calculus - don't know if this is helpful for the class/watch?v=CtRAHmeWSC0&featu re=relmfuAnother good resource is Gil Strang's excellent MIT Linear Algebra course/courses/mathematics/18-06-linear-algebra-spr ing-2010An interesting (and funny) lecture discussing limitations of the neural networks:/user/GoogleTechTalks#p/search/0/Ayz OUbkUf3MScientific books and papers on AI. Interesting, but advanced: /subject/computer-and-information-science/artificial-intelligence/The Matrix and Quaternions FAQ/documents/matrfaq.html。