下面是搜集的一些机器学习的课程资源：
1. General Machine learning courses
1.1 Introduction to Machine Learning courses
Coursera上 Andrew Ng的machine learning课，现在是self learning，可以随时学习。
1.2 Machine Learning courses @ Universities with videos
Andrew Ng在Stanford开设的CS 229: Machine Learning课程，notes很好，多看几遍收获很大。
来自Washington的Pedro Domingos在Coursera上面开设的Machine Learning，Domingos大牛可谓机器学习界风云人物，发明了Sum-Product Network, Markov Logic等等模型，我等渣渣只能在大牛的脚下做一些小的improvement。
CMU院士Tom Mitchell开设的10-701/15-781
1.3 Machine Learning courses @ Universities without videos
MIT 6.867 研究生课程Machine Learning，notes很详细！
2. Advanced machine learning courses
高级机器学习课程，包括图模型，无参贝叶斯，统计机器学习理论
2.1 Probabilistic Graphical Models
来自CMU的Eric Xing在2014年春季开设的概率图模型：Probabilistic Graphical Models
(Spring 2014)
Stanford的Daphne Koller在coursera开设的Probabilistic Graphical Models
2.2 Statistical Learning Theory
CMU大牛Larry Wasserman的统计机器学习Statistical Machine Learning
2.3 一些比较老的资源
2.3.1 Copied from Dr. Tomasz.
以下资源都是从Dr. Tomasz主页分享而获得的，
With videos
Graduate Summer School: Intelligent Extraction of Information from Graphs and High Dimensional Data.
UCLA Institute for Pure & Applied Mathematics.
July 2005
(He highly recomment the Michael Jordan Graphical Model videos!!!)
Emphasis Week on Learning and Inference in Vision
February 2005
Simoncelli, Mumford, Fitzgibbon, Efros, Frey, Zhu,
Freeman, Black, Blake, Isard, Weiss, Huttenlocher,
Yuille, Zabih, Besag, Gottardo, Donoho
MSRI
With notes
CS 281B / Stat 241B: Statistical Learning Theory
Spring 2004
Michael Jordan
Berkeley
CS 281A / Stat 241A: Statistical Learning Theory
Fall 2004
Michael Jordan
Berkeley
9.520: Statistical Learning Theory and Applications
Spring 2014
Tomas Poggio et al
MIT
Spring 2003
Statistics 315B: Modern Applied Statistics:
Elements of Statistical Learning II
Jerome H. Friedman
Stanford
Probabilistic Graphical Models
Fall 2004
Kevin Murphy
UBC
2.3.2 Some Graduate school videos and old workshops.
With videos
Deep Learning Workshop: Foundations and Future Directions
Special Topics in Computer Vision04 Spring