• Matrix cookbook

• Online access to Carl Meyer’s book: Matrix analysis and applied linear algebra

• Carlo Tomasi lecture notes

• Stanford workshop on algorithms for modern massive data (MMDS)

• 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 Hofmann: Machine Learning and Pattern Recognition

9. Daphne Koller: Probabilistic Models in Artificial Intelligence

10. Tom Mitchell: Machine Learning

11. Andrew Ng: Machine Learning

• ICCV course on Learning and Vision

• Machine Learning Summer Schools 2005, 2004, 2003

• Computer Vision

1. Intel Open Source Computer Vision (OpenCV) Library

2. CVonline: Compendium of Computer Vision

3. Keith Price Annotated Computer Vision Bibliography

• Code:

1. Netlab: http://www.ncrg.aston.ac.uk/netlab/over.php

2. Torch: http://www.idiap.ch/machine-learning.php

3. Weka: http://www.cs.waikato.ac.nz/ml/weka/

4. Factor analysis and mixture of factor analyzers: ftp://ftp.cs.toronto.edu/zoubin/mfa.tar.gz

5. Isomap: http://isomap.stanford.edu/

6. LLE: http://www.cs.toronto.edu/~roweis/lle/code.html

7. Locally linear coordination: http://www.cs.berkeley.edu/~ywteh/research/llc/

8. SVM: see kernel machine

9. Adaboost: See boost algorithms

• Manifold Learning

• Neural Information Processing Systems

• Kernel Machine

• Boosting Algorithms

• Gaussian Process

• Variational Learning