= Machine Learning Pages =

These pages have been compiled by members of the CBU Learning Machine Learning (LML) Group

== Machine Learning Course ==

1. Introduction (applications, supervised, unsupervised, semi-supervised, reinforcement learning, bayes rule, probability theory, randomness) attachment:Presentation1_LML.ppt, 27 May 2008, Eleftherios Garyfallidis.

2. Further Introduction (what is ML, bayes rule, bayesian regression,entropy, relative entropy, mutual information), attachment:Presentation2_LML.ppt, 3 June 2008, Eleftherios Garyfallidis.

3. Maximum Likelihood vs Bayesian Learning (Notes available upon request) attachment:Presentation3_LML.ppt, 10 June 2008, Hamed Nili.

4. Factor Analysis, PCA and pPCA, attachment:Presentation4_LML.ppt, 17 June 2008, Hamed Nili.

5. Independent Component Analysis (ICA), attachment:Presentation5_LML.pdf, 24 June 2008, Jason Taylor.

6. ICA and Expectation Maximization (EM), 1 July 2008.

7. Graphical Models ..., 8 July 2008, Ian Nimmo-Smith.


== Books ==

1. Pattern Recognition and Machine Learning, C. M. Bishop, 2006.

2. Information Theory and Learning Algorithms, D. J. C. Mackay, 2003.

3. Netlab Algorithms for Pattern Recognition, I. T. Nabney, 2001.

== Reading ==

=== ICA vs PCA ===

http://genlab.tudelft.nl/~dick/cvonline/ica/node3.html

=== ICA ===

http://www.cs.helsinki.fi/u/ahyvarin/papers/NN00new.pdf 

=== MCMC ===

Christophe Andrieu, Nando de Freitas, Arnaud Doucet and Michael I. Jordan. (2003) [attachment:Andrieu2003.pdf An Introduction to MCMC for Machine Learning.] Machine Learning, 50, 5–43, 2003.

=== Bayes Rule  ===

Highly recommended Bishop's book first chapter 1.2.

http://plato.stanford.edu/entries/bayes-theorem/

[http://cocosci.berkeley.edu/tom/papers/tutorial2.pdf Thomas Griffiths,  Alan Yuille. A Primer on Probabilistic Inference. ]

[http://yudkowsky.net/bayes/bayes.html An Intuitive Explanation of Bayesian Reasoning Bayes' Theorem By Eliezer Yudkowsky]

[http://homepages.wmich.edu/~mcgrew/Bayes8.pdf Eight versions of Bayes' theorem]

=== Bayesian Methods in Neuroscience ===

[http://www.gatsby.ucl.ac.uk/~pel/papers/ppc-06.pdf Ma, W.J., Beck, J.M., Latham, P.E. & Pouget, A.  (2006) Bayesian inference with probabilistic population codes. Nature Neuroscience. 9:1432-1438]

[http://cocosci.berkeley.edu/tom/papers/bayeschapter.pdf Griffiths,Kemp and Tenenbaum. Bayesian models of cognition.]

[http://www.cvs.rochester.edu/knill_lab/publications/TINS_2004.pdf Knill, D. C., & Pouget, A. (2004). The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosciences, 27(12), 712-719.]

[http://www.inf.ed.ac.uk/teaching/courses/mlsc/HW2papers/koerdingTiCS2006.pdf Kording, K. & Wolpert, D.M. (2006) Bayesian decision theory in sensorimotor control. TRENDS in Cognitive Sciences,10, 319-326]







=== Software ===

Public code for machine learning :

http://homepages.inf.ed.ac.uk/rbf/IAPR/researchers/MLPAGES/mlcode.htm