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