http://videolectures.net，例如输入Zoubin
Coursera
Machine learninghttps://www.coursera.org/learn/machine-learning/ I. Introduction (Week 1): watch more than twice (能闭目听懂，绝不再看)
II. Linear Regression with One Variable (Week 1):已看一次，内容很容易，都已理解
III. Linear Algebra Review (Week 1, Optional):绝不看，都是基本内容。20160711看两次Lecture14 Dimensionality Reduction (Week 8)的ppt, understand completely,故视频暂不看。
IV. Linear Regression with Multiple Variables (Week 2)和Lecture6 Logistic Regression (Week 3), Lecture7 Regularization (Week 3), Lecture8 Neural Networks Representation (Week 4), Lecture9 Neural Networks Learning (Week 5),
Lecture10 Advice for Applying Machine Learning (Week 6), Lecture11 Machine Learning System Design (Week 6), Lecture12 Support Vector Machines (Week 7), Lecture17 Large Scale Machine Learning (Week 10)已看两次，内容很容易，都已理解，绝不再看。Lecture12在20160711看完
Lecture5 Octave Tutorial暂不看(百度百科：Octave是一种高级语言，主要设计用来进行数值计算，它是MathWorks出品的Matlab商业软件的一个强有力的竞争产品。)
20150814将Lecture7 Regularization (Week 3)之前都已经复习两次，并且整理出复习提纲。20150830和20150904将Lecture 10分别复习一次，绝不再看。20150904和20150912将Lecture 11和Lecture 17分别复习一次，绝不再看。
下面看Lecture13 Clustering (Week 8)
，如果课件全懂，绝不看;一定得先看以前的模式笔记,Zhihua教材, Ming Zhu教材和笔记, Jian Pei笔记。
Lecture15
Anomaly Detection，Lecture16
Recommender Systems和Lecture18即是扩充知识面，又是练英语
学法：先看无字幕版，再看有字幕版，每个最少最少看两篇，直至闭目听懂。 一定认真听记笔记，很多课程都仅听一次
Valse QQ群：张兴国T-秋田大(582968216) 2017/8/31 6:39:18
吴恩达在
http://coursera.org/
上的《神经网络和深度学习》这门课在网易云课堂上可以免费看了，还有中文字幕，感兴趣的老师和同学可以去看看
应该是这个https://www.coursera.org/learn/neural-networks-deep-learning#
https://mooc.study.163.com/smartSpec/detail/1001319001.htm/?utm_source=weibo.com&utm_medium=timeline&utm_campaign=deepLearning&utm_content=wnd20170831
韩家炜教授数据挖掘龙星课程视频
http://www.youku.com/playlist_show/id_1903290.html 韩家炜教授Pattern Discovery in Data Mining
https://www.coursera.org/course/patterndiscovery Si Liu老师在VALSE20150813-Panel推荐的Boyd的Convex Optimization课程
http://www.youtube.com/watch?v=McLq1hEq3UY;
http://stanford.edu/~boyd/cvxbook/。一个是视频，一个是video
Mingming Chen(Nankai) recomm
end a course in Valse QQ群：牛津大学机器学习课程（PPT，讲课视频，作业，代码等）：
https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/ Fei-Fei Li: How we're teaching computers to understand pictures
http://www.ted.com/talks/fei_fei_li_how_we_re_teaching_computers_to_understand_pictures#
[转自静.沙龙]斯坦福人工智能实验室主任、计算机视觉实验室主任Fei-Fei Li教授本月的计算机视觉TED Talk: 
http://t.cn/RAwE22v 不错的科普教材。
美国大学线上课件大全: http://mp.weixin.qq.com/s?__biz=MzA5NzcxMzc4OQ==&mid=214275160&idx=2&sn=6e1377d47db154eb7507c6eac6f4ad26&scene=1&from=groupmessage&isappinstalled=0#rd
What we’re learning from online education: https://www.ted.com/talks/daphne_koller_what_we_re_learning_from_online_education/transcript
English teacher Tarey of Umich recommends "2016 University of Waterloo 3MT first prize winner: Gah-Jone Won". MT is short for minutes thesis. If you search "Gah-Jone Won" on youtube, you will find it.很好的科普
徐亦达老师机器学习视频网站
http://www.valser.org/thread-725-1-1.html http://www-staff.it.uts.edu.au/~ydxu/statistics.htm Valse qq群有人说：
徐老师 讲的很仔细
徐亦达老师20180407在微信群发:
大家好，最近把我的机器学习课程讲义,代码，视频链接都移植到了 github 上啦 https://github.com/roboticcam/machine-learning-notes/blob/master/README.md 我以后一定每过两天就更新一下
徐亦达老师20180513在微信群发:
大家好，为了准备7月在北航上32课时300人的机器学习课，我最近一直在努力更新课件。上个星期加了一个word representation and softmax, 讲了一些自然语言处理的数学问题，包括 word2vec 优化, noise contrastive estimation, negative sampling, Gumbel max trick 等。还在努力完善中
https://github.com/roboticcam/machine-learning-notes/blob/master/word_vector.pdf
谢谢大家。这也是我第一次写NLP 课件。[Shy][Shy]我们做了一些的nlp 工业项目。我打算趁去北航的机会。花点时间整理一下
Terry Tang: https://www.youtube.com/watch?v=SK8FRKBb2FI
徐亦达T悉尼科大<xuyida@hotmail.com> 2018/5/20 8:26:58 (Valse qq群)
大家好
整整两年半没有发新的机器学习视频啦。今天终于痛下决心
从此开始坚持每星期的视频和讲义的更新。优酷地址
http://i.youku.com/u/UMzIzNDgxNTg5Ng YouTube地址
https://www.youtube.com/channel/UConITmGn5PFr0hxTI2tWD4Q 讲义网址
https://github.com/roboticcam/machine-learning-notes/blob/master/README.md
张志华老师：
机器学习导论 http://ocw.sjtu.edu.cn/G2S/OCW/cn/CourseDetails.htm?Id=397
统计机器学习http://ocw.sjtu.edu.cn/G2S/OCW/cn/CourseDetails.htm?Id=398
2017年02月17日邮件:
https://www.ini.rub.de/PEOPLE/wiskott/Teaching/Material/index.html