· Online Machine Learning Lecture Notes:
1. Michael Jordan: Advanced Topics in Learning and Decision Making, other courses
2. Geoffrey Hinton: Summer School Lectures, Introduction to Neural Networks and Machine Learning
3. Christopher Bishop: Machine Learning Techniques for Computer Vision
4. Zoubin Ghahramani: Unsupervised Learning, Statistical Approaches to Learning and Discovery
5. Sam Roweis: Machine Learning, Uncertainty and Learning in Artificial Intelligence, other courses
6. Tommi Jakkola: Introduction to Neural Networks and Machine Learning, Machine Learning, other courses
7. Lawrence Saul: Artificial Intelligence and Machine Learning, Advanced Topics in Artificial Intelligence and Machine Learning,
8. Thomas Hoffmann: Machine Learning and Pattern Recognition
9. Daphne Koller: Probabilistic Models in Artificial Intelligence
10. Andrew Ng: Machine Learning
11. ICCV course on Learning and Vision
12. Machine Learning Summer Schools 2005, 2004, 2003
· Code:
1. Factor analysis and mixture of factor analyzers: ftp://ftp.cs.toronto.edu/zoubin/mfa.tar.gz
2. Isomap: http://isomap.stanford.edu/
3. LLE: http://www.cs.toronto.edu/~roweis/lle/code.html
4.
Locally linear coordination: http://www.cs.berkeley.edu/~ywteh/research/llc/
5. Netlab: http://www.ncrg.aston.ac.uk/netlab/over.php
· Neural Information Processing Systems