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Friday, September 27, 2013

Bayesian non-parametrics idea

 I dont know why but this tutorial is really approachable and makes the idea clear.
Awesome high level overview of bayesian non-parametrics by Zoubin Ghahramani
http://mlg.eng.cam.ac.uk/pub/pdf/Gha12.pdf
https://www.ee.washington.edu/techsite/papers/documents/UWEETR-2010-0006.pdf

 CVPR2012 tutorial applied bayesian non-parametrics
 http://cs.brown.edu/~sudderth/bnpCVPR12/materials.html
 http://cs.brown.edu/~sudderth/bnpCVPR12/resources.html
 http://cs.brown.edu/courses/csci2950-p/lectures.html
http://www.cs.princeton.edu/courses/archive/fall07/cos597C/syllabus.html

Topics in Machine Learning: Bayesian Methods for Machine Learning
http://www.cs.utoronto.ca/~radford/csc2541.S11/ 

http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/nonparametric/

Best place for DP chapter 2 of thesis
http://cs.brown.edu/~sudderth/papers/sudderthPhD.pdf

http://www.cs.toronto.edu/~radford/mixmc.abstract.html
        http://www.cs.toronto.edu/~radford/ftp/bmm.pdf
        http://www.cs.utoronto.ca/~radford/ftp/review.pdf
        http://www.cs.toronto.edu/~radford/ftp/mixmc.pdf
        http://www.cs.toronto.edu/~radford/ftp/mixsplit.pdf
        http://www.cs.berkeley.edu/~jordan/papers/hdp.pdf
        http://books.nips.cc/papers/files/nips18/NIPS2005_0130.pdf
        http://books.nips.cc/papers/files/nips19/NIPS2006_0716.pdf

http://npbayes.wikidot.com/references

Saturday, September 21, 2013

Awesome Video Lectures (I liked) @ videolectures.net

Approximate Inference(Expectation Propagation)  by Tom Minka
http://videolectures.net/mlss09uk_minka_ai/#c3275

Topic Models by David Blei
http://videolectures.net/mlss09uk_blei_tm/

Graphical Models and message-passing algorithms by Wainwright
http://videolectures.net/mlss2011_wainwright_messagepassing/

MCMC by murray
http://videolectures.net/mlss09uk_murray_mcmc/

Bayesian Inference and Gaussian Process by Carl Rasmussen
http://videolectures.net/mlss07_rasmussen_bigp/

Structure prediction: A large margin approach by ben taskar
http://videolectures.net/aop07_taskar_pgm/

Algorithms for Predicting Structured Data(OK for beginner, intuitive)
http://videolectures.net/ecmlpkdd2010_gartner_vembu_apsd/

Nonparametric bayesian
http://videolectures.net/mlss2011_teh_nonparametrics/
http://videolectures.net/mlss09uk_teh_nbm/
http://videolectures.net/mlss09uk_orbanz_fnbm/
http://videolectures.net/mlss06au_tresp_dpnbm/

Dirichlet Processes: Tutorial and Practical Course
http://videolectures.net/mlss2012_gorur_dirichlet_practical/
http://videolectures.net/mlss07_teh_dp/

Dimentionality Reduction by Neil Lawrence
http://videolectures.net/mlss2012_lawrence_dimensionality_reduction/

Support Vector Machines
http://videolectures.net/mlss06tw_lin_svm/

Low Rank Modelling by Emmanuel Candes
http://videolectures.net/mlss2011_candes_lowrank/

Geometric Methods and Manifold Learning by Niyogi
http://videolectures.net/mlss09us_niyogi_belkin_gmml/

MAP inference in Discrete Models(Max flow / st cut / submodularity)
http://videolectures.net/bmvc2012_kohli_discrete_models/

Tuesday, September 10, 2013

Latent Dirichilet Allocation and variational method

I made a presentation on Latent Dirichlet Allocation with its inference and mean field method for study group.
I thought I would share it so it might be of some help to someone. It is assumed you know basic Inference, basic probability and standard distributions, EM algorithm, basic sampling.
https://www.dropbox.com/s/qsh8seuy527z30s/variational.pdf 

If you think some credits might be missing then mail me.