Visual Recognition and Search Topics in Information Processing

http://rogerioferis.com/VisualRecognitionAndSearch/Resources.html

Selected links for probabilistic models

NPBAYES resourses by peterorbanz
http://stat.columbia.edu/~porbanz/talks/npb-tutorial.html

Glossary by tom minka
http://alumni.media.mit.edu/~tpminka/statlearn/glossary/

PGM by murphy
http://www.cs.ubc.ca/~murphyk/Teaching/Stat521A-Spring09/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

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

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/

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.

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

Path to becoming Data Scientist - Metro Map

Source: http://nirvacana.com/thoughts/becoming-a-data-scientist/

Latex symbol classifier

I saw this interesting site which classifies Latex symbols and gives its command.
http://detexify.kirelabs.org/classify.html

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 intermediate-level 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 Large-Sample 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

Probabilistic programming

This is nice a nice post about probabilistic programming.
http://radar.oreilly.com/2013/04/probabilistic-programming.html
http://tm.durusau.net/?cat=413

http://zinkov.com/posts/2012-06-27-why-prob-programming-matters/

workshop and tutorials
http://projects.csail.mit.edu/church/wiki/Probabilistic_Models_of_Cognition
http://projects.csail.mit.edu/church/wiki/Church
http://probabilistic-programming.org/wiki/NIPS*2012_Workshop
http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Chapter1_Introduction.ipynb

Probabilistic Programming and Bayesian Methods for Hackers Using Python and PyMC

https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
Collection of ipython notebooks
https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks