Reading online notes and doing problems from other professors' course webpages is the best way to learn ML!

Here is a collection of links from schools such as CMU,Berkeley,MIT,Stanford,Brown,etc

They are roughly sorted by some arcane criterion which roughly corresponds to how useful I found them to be.

On the bottom of this page you can find some links related to vision and learning. These aren't your everyday computer vision links, only learning based vision!

• Graduate Summer School: Intelligent Extraction of Information from Graphs and High Dimensional Data. UCLA Institute for Pure & Applied Mathematics. July 2005 (I highly recomment the Michael Jordan Graphical Model videos!!!) https://www.ipam.ucla.edu/schedule.aspx?pc=gss2005
• 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 http://www.msri.org/calendar/workshops/WorkshopInfo/298/show_workshop
• Martin Wainwright's Lecture Webcasts EECS 281A: Statistical Learning Theory - Graphical Models Fall 2005 http://inst.eecs.berkeley.edu/~cs281a/fa05/lectures/lectures.html

• CS 281B / Stat 241B: Statistical Learning Theory Spring 2004 Michael Jordan Berkeley http://www.cs.berkeley.edu/~jordan/courses/281B-spring04/
• CS 281A / Stat 241A: Statistical Learning Theory Fall 2004 Michael Jordan Berkeley http://www.cs.berkeley.edu/~jordan/courses/281A-fall04/
• 9.520: Statistical Learning Theory and Applications Spring 2004 Tomas Poggio et al MIT http://www.mit.edu/~9.520/ Spring 2003 http://www.mit.edu/afs/athena/course/9/9.520/www/spring03/
• 10-702 CALD: Statistical Foundations of Machine Learning Spring 2005 John Lafferty and Larry Wasserman http://www.cs.cmu.edu/~10702/
• 10-7072 CALD: Statistical Approaches to Learning and Discovery Spring 2003 John Lafferty, Tom Mitchell et al http://www.cs.cmu.edu/%7Etom/702.html
• CS 295-3: Machine Learning & Pattern Recognition Thomas Hofmann Brown http://www.cs.brown.edu/courses/cs295-3/
• ECE695 Statistical Learning Theory Fall 2003 Cornell Shai Ben David http://www.csl.cornell.edu/courses/ece695n/
• 6.867 Machine Learning (Fall 2004) Tommi Jaakkola MIT http://www.ai.mit.edu/courses/6.867-f04/
• Statistics 315B: Modern Applied Statistics: Elements of Statistical Learning II Jerome H. Friedman Stanford http://www.stanford.edu/class/stats315b/
• Introduction to Neural Networks Spring 2005 MIT Sebastian Seung http://hebb.mit.edu/courses/9.641/
• Graphical Models and Bayesian Networks Bernt Schiele Fall 2002 ETH Zurich http://www.vision.ethz.ch/gm/gm.html
• BS2 Statistical Inference 2004 Steffen L. Lauritzen Oxford http://www.stats.ox.ac.uk/~steffen/bs2siMT04/index.htm
• Probabilistic Graphical Models Fall 2004 Kevin Murphy UBC http://www.cs.ubc.ca/~murphyk/Teaching/CS532c_Fall04/index.html
• Machine Learning CS 229 Andrew Ng Stanford Autumn 2005 http://www.stanford.edu/class/cs229/

• Online Machine Learning book Information Theory, Inference, and Learning Algorithms by David MacKay http://wol.ra.phy.cam.ac.uk/mackay/itila/book.html
• Convex Optimization Stephen Boyd and Lieven Vandenberghe http://www.stanford.edu/~boyd/cvxbook/

• ICCV Course on Learning and Vision October 12, 2003 Andrew Blake & Bill Freeman http://people.csail.mit.edu/billf/learningvision/
• ICCV Course on Recognizing and Learning Object Categories October 2005 Li Fei-Fei, Rob Ferbus, Antonio Torralba http://people.csail.mit.edu/torralba/iccv2005/
• ICCV tutorial on Markov Chain Mote Carlo for Computer Vision ICCV 2005 Song-Chun Zhu, Delleart and Tu http://civs.stat.ucla.edu/MCMC/MCMC_tutorial.htm