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Popular Posts

I have been learning ML for sometime now and I have spent some time on finding what are some of the good resources for ML. For Beginners...

I could not setup the compiler for myself to create mex files. So I searched the internet, going through many blogs and posts. Finally I go...

Unsupervised feature learning and deep learning has been fascinating to me recently and here are some interesting links and tutorials. F...

After 2 days of nonstop struggle, I was able to make theano work with my GPU NVIDIA GeForce GTX 860M. TRY AT YOUR OWN RISK!! Settings ...

This is nice a nice post about probabilistic programming. http://radar.oreilly.com/2013/04/probabilisticprogramming.html http://tm.durus...
Thursday, October 17, 2013
Friday, October 11, 2013
Selected links for probabilistic models
NPBAYES resourses by peterorbanz
http://stat.columbia.edu/~porbanz/talks/npbtutorial.html
Glossary by tom minka
http://alumni.media.mit.edu/~tpminka/statlearn/glossary/
PGM by murphy
http://www.cs.ubc.ca/~murphyk/Teaching/Stat521ASpring09/index.html
Probabilistic graphical models  advanced methods by murphy
https://sites.google.com/site/cs228tspring2012/
STA561: Probabilistic Machine Learning: Fall 2013
http://genome.duke.edu/labs/engelhardt/courses/sta561.html
http://stat.columbia.edu/~porbanz/talks/npbtutorial.html
Glossary by tom minka
http://alumni.media.mit.edu/~tpminka/statlearn/glossary/
PGM by murphy
http://www.cs.ubc.ca/~murphyk/Teaching/Stat521ASpring09/index.html
Probabilistic graphical models  advanced methods by murphy
https://sites.google.com/site/cs228tspring2012/
STA561: Probabilistic Machine Learning: Fall 2013
http://genome.duke.edu/labs/engelhardt/courses/sta561.html
Friday, September 27, 2013
Bayesian nonparametrics idea
I dont know why but this tutorial is really approachable and makes the idea clear.
Awesome high level overview of bayesian nonparametrics by Zoubin Ghahramani
http://mlg.eng.cam.ac.uk/pub/pdf/Gha12.pdf
https://www.ee.washington.edu/techsite/papers/documents/UWEETR20100006.pdf
CVPR2012 tutorial applied bayesian nonparametrics
http://cs.brown.edu/~sudderth/bnpCVPR12/materials.html
http://cs.brown.edu/~sudderth/bnpCVPR12/resources.html
http://cs.brown.edu/courses/csci2950p/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/294fall09/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
Awesome high level overview of bayesian nonparametrics by Zoubin Ghahramani
http://mlg.eng.cam.ac.uk/pub/pdf/Gha12.pdf
https://www.ee.washington.edu/techsite/papers/documents/UWEETR20100006.pdf
CVPR2012 tutorial applied bayesian nonparametrics
http://cs.brown.edu/~sudderth/bnpCVPR12/materials.html
http://cs.brown.edu/~sudderth/bnpCVPR12/resources.html
http://cs.brown.edu/courses/csci2950p/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/294fall09/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 messagepassing 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/
http://videolectures.net/mlss09uk_minka_ai/#c3275
Topic Models by David Blei
http://videolectures.net/mlss09uk_blei_tm/
Graphical Models and messagepassing 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.
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.
Thursday, August 29, 2013
Bayesian Critique of Statistics in Health: The Great Health Hoax
Yet another support for bayesians:
http://www2.isye.gatech.edu/~brani/isyebayes/bank/pvalue.pdf
http://www2.isye.gatech.edu/~brani/isyebayes/bank/pvalue.pdf
Monday, August 12, 2013
Sunday, July 28, 2013
Latex symbol classifier
I saw this interesting site which classifies Latex symbols and gives its command.
http://detexify.kirelabs.org/classify.html
http://detexify.kirelabs.org/classify.html
Sunday, April 28, 2013
From Mike Jordan on what people should learn for ML.
From a post from news.ycombinator.com
I personally think that everyone in machine learning should be (completely) familiar with essentially all of the material in the following intermediatelevel statistics book:
1.) Casella, G. and Berger, R.L. (2001). "Statistical Inference" Duxbury Press.
For a slightly more advanced book that's quite clear on mathematical techniques, the following book is quite good:
2.) Ferguson, T. (1996). "A Course in Large Sample Theory" Chapman & Hall/CRC.
You'll need to learn something about asymptotics at some point, and a good starting place is:
3.) Lehmann, E. (2004). "Elements of LargeSample Theory" Springer.
Those are all frequentist books. You should also read something Bayesian:
4.) Gelman, A. et al. (2003). "Bayesian Data Analysis" Chapman & Hall/CRC.
and you should start to read about Bayesian computation:
5.) Robert, C. and Casella, G. (2005). "Monte Carlo Statistical Methods" Springer.
On the probability front, a good intermediate text is:
6.) Grimmett, G. and Stirzaker, D. (2001). "Probability and Random Processes" Oxford.
At a more advanced level, a very good text is the following:
7.) Pollard, D. (2001). "A User's Guide to Measure Theoretic Probability" Cambridge.
The standard advanced textbook is Durrett, R. (2005). "Probability: Theory and Examples" Duxbury.
Machine learning research also reposes on optimization theory. A good starting book on linear optimization that will prepare you for convex optimization:
8.) Bertsimas, D. and Tsitsiklis, J. (1997). "Introduction to Linear Optimization" Athena.
And then you can graduate to:
9.) Boyd, S. and Vandenberghe, L. (2004). "Convex Optimization" Cambridge.
Getting a full understanding of algorithmic linear algebra is also important. At some point you should feel familiar with most of the material in
10.) Golub, G., and Van Loan, C. (1996). "Matrix Computations" Johns Hopkins.
It's good to know some information theory. The classic is:
11.) Cover, T. and Thomas, J. "Elements of Information Theory" Wiley.
Finally, if you want to start to learn some more abstract math, you might want to start to learn some functional analysis (if you haven't already). Functional analysis is essentially linear algebra in infinite dimensions, and it's necessary for kernel methods, for nonparametric Bayesian methods, and for various other topics. Here's a book that I find very readable:
12.) Kreyszig, E. (1989). "Introductory Functional Analysis with Applications" Wiley.
Source: https://news.ycombinator.com/item?id=1055389
http://homepages.inf.ed.ac.uk/sgwater/reading_list.html
http://cocosci.berkeley.edu/tom/bayes.html#general
Video lectures on functional analysis:
http://www.youtube.com/watch?v=ebesx6pF8mg&list=PLBC73B96341ECF455
http://www.youtube.com/watch?v=7IIw_U8rv4Q&list=PL2B92DCEAB0A249CD
Monday, April 22, 2013
Probabilistic programming
This is nice a nice post about probabilistic programming.
http://radar.oreilly.com/2013/04/probabilisticprogramming.html
http://tm.durusau.net/?cat=413
http://zinkov.com/posts/20120627whyprobprogrammingmatters/
workshop and tutorials
http://projects.csail.mit.edu/church/wiki/Probabilistic_Models_of_Cognition
http://projects.csail.mit.edu/church/wiki/Church
http://probabilisticprogramming.org/wiki/NIPS*2012_Workshop
http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/ProbabilisticProgrammingandBayesianMethodsforHackers/master/Chapter1_Introduction/Chapter1_Introduction.ipynb
Collection of ipython notebooks
https://github.com/ipython/ipython/wiki/AgalleryofinterestingIPythonNotebooks
http://radar.oreilly.com/2013/04/probabilisticprogramming.html
http://tm.durusau.net/?cat=413
http://zinkov.com/posts/20120627whyprobprogrammingmatters/
workshop and tutorials
http://projects.csail.mit.edu/church/wiki/Probabilistic_Models_of_Cognition
http://projects.csail.mit.edu/church/wiki/Church
http://probabilisticprogramming.org/wiki/NIPS*2012_Workshop
http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/ProbabilisticProgrammingandBayesianMethodsforHackers/master/Chapter1_Introduction/Chapter1_Introduction.ipynb
Probabilistic Programming and Bayesian Methods for Hackers Using Python and PyMC
https://github.com/CamDavidsonPilon/ProbabilisticProgrammingandBayesianMethodsforHackersCollection of ipython notebooks
https://github.com/ipython/ipython/wiki/AgalleryofinterestingIPythonNotebooks
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