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
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://homepages.wmich.edu/~mcgrew/Bayes8.pdf Eight versions of 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]
Bayesian 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