## Monday, May 14, 2012

### Using multiple functions in single file(globally)

Declaration: 
function funs = makefuns
funs.fun1=@fun1;
funs.fun2=@fun2;
end

function y=fun1(x)
...
end

function z=fun2
...
end
 
 
 
Calls: 
myfuns = makefuns;
myfuns.fun1(x)
myfuns.fun2() 


see also for object oriented programming in matlab: 
http://yagtom.googlecode.com/svn/trunk/html/objectOriented.html

### Machine Learning Resources for Beginners and Beginners++

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 would say if you look beyond beginners section  you might be overwhelmed by the amount of content. Just relax and eat it bit by bit. This is not a race.

My most visited page was random copied resource list on Machine Learning. As now I have some idea about what all the links are about I will try to reorganize the page with time(Possibly subcategorize).
Pre-requisites
• Programming
Before starting I would recommend to learn some programming which also supports Numerical operations like matrix multiplications or basic numerical operations like matrix decompositions etc(Do not worry if you dont know these terms, learn to program and focus on that). I would suggest to learn Python with libraries called Numpy and Scipy. Other alternatives are Matlab/Octave/R. For C++ Armadillo is a great option which seems promising to me.
• AlgorithmsKnowing basic algorithms is essential. It also help you implement ideas to programs. Any algorithms course would be enough. There are few at coursera. Any one of the 2nd or 3rd set is good enough.
Set 1: Algorithmic Thinking
Set 2: Algorithms, Part I & Part II by robert sedgewick(Princeton) at Coursera
Set 3: Algorithms: Design and Analysis, Part 1 & Part 2 by tim roughgarden(Stanford) at Coursera

• Calculus
Calculus is very very essential pre-requisite to Machine Learning. You can always come back for lookups.
• ProbabilityAnyone of the three should be good enough. You can always come back for lookups.  Probability Primer by Mathematical Monk at Youtube
Machine Learning Lectures:
Beginners
1.  Machine Learning Course by Andrew Ng at Coursera
Thinking about what should be the first course in ML. The only course that comes to my mind is Machine Learning Course by Andrew Ng at Coursera. Practically no prerequisites(maybe Calculus). I highly recommend doing the programming exercises. Keeps your interests going. Not too mathematical. There is also a very short tutorial on Octave/Matlab(Just enough for doing Homeworks).
2.  Linear Algebra Course by Gilbert Strang
(I believe the best on internet)
We also need to understand about spaces. This is a good place to get the intuition about vector spaces. Video Lectures accompanies a text book. I would also like to suggest lecture notes by vandenberge (pdf) which are great for implementing various matrix factorizations as recursive solution.
3. Learning from Data by Abu Mostafa at Caltech
Course is more detailed than ML by Andrew Ng at Coursera. Very clear explanation of the content. Video Lectures accompanies a textbook.
4.  Linear and Integer Programming by Sriram Sankaranarayanan at Coursera/UoColorado
To start getting a flavour of optimization this is an excellent place to start. This also shows the advantage of abstraction in solving problem.
Beginners++
1.  Machine Learning by Andrew Ng at Stanford SEE
This is a longer version of classroom course taught by Andrew Ng at Stanford. This course covers a lot of topics not covered in above courses. This courses accompanies an excellent set of lecture notes(Highly recommended).
see lecture notes because sometimes lectures are not clearly explained but still is good for introductory ML.
2.  Probabilistic Graphical Models by Daphne Koller at Coursera
http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=ProbabilisticGraphicalModels
This course is like marriage of Probability and Graph Theory which is a significant chunk in Machine Learning. It involves efficient inference methods and how graphs help us. The programming assignments are not very easy like previous courses. This is widely used in NLP and Computer Vision.
3. Machine learning  Lectures by Mathematical Monk
If wondering who is he, then visit http://www.dam.brown.edu/people/jmiller/teaching.html
This is a course covering a lot of methods similar to Machine Learning Course by Andrew Ng. But the methods covered are wider(Also more probabilistic) than Andrew Ng's ML course.
4. Introduction to ML This and This by Alex Smola 10-701 at CMU
This is a foundation course for PhD students.

5. Scalable Machine Learning by Alex Smola
http://alex.smola.org/teaching/berkeley2012/
This course deals with Scalablity issues and jargons in Machine Learning. It is also good for Statistics, Graphical Models, Recommender Systems, Kernels. This course should be complemented with a practical Project considering Scalability issues.
6. Introduction to Recommender Systems
Not too difficult course but the whole course is on recommender Systems.
7. Machine Learning By Pedro Domingos at Coursera/UoW
This course also covers lot of topics well explained. This could be done independently(Worth considering atleast for the topics not covered in previous course)
8. Mining Massive Data Sets by Jure Leskovec at Coursera/Stanford
Deals with scalability issues of ML/Datamining.
9.  Neural Networks for Machine Learning by Geoffrey Hinton
Great introductory course to Neural Network. This course has included all the recent advanced in NNs. At the end of the course it might get difficult to understand. You might need to complement the end of the course with some ML text on inference(including approximate inference).
10. Deep Learning by Yann Lecunn
Deep Learning course dealing with practicality. Doing work on GPU etc.
11.  Neural Networks by Hugo Larochelle
This is a fast paced course in Neural Networks. Great if you have some background of bits and pieces of NN/Inference/Graphical Models.
12.  Deep Learning by Nando D. F. Oxford
https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/
13.  Wow course on reinforcement learning by david silverhttps://www.youtube.com/playlist?list=PL5X3mDkKaJrL42i_jhE4N-p6E2Ol62Ofa
14.  Wow course on approximate dynamic programming AKA Reinforcement Learning by Dimitri P. Bertsekas https://www.youtube.com/playlist?list=PLiCLbsFQNFAxOmVeqPhI5er1LGf2-L9I4
http://web.mit.edu/dimitrib/www/DP_Slides_2015.pdf
http://web.mit.edu/dimitrib/www/Abstract_DP_Online_Complete.pdf (Beauty)
http://arxiv.org/abs/1405.6757 Proximal RL
15. Advanced Inference  / Submodular functions course by Jeff Bilmes
1. Big Data, Large Scale Machine Learning by Langford, Lecunn
Again a practical course for dealing with scalability,
online learning etc.
2. Convex Optimization I  by Stephen Boyd at Stanford SEE

CVX101 Convex Optimization by Stephen Boyd

A great course on Convex Optimization. I would not say this is an easy course. But totally worth the effort put in. This also complements a text book freely available.
3. Convex Optimization II  by Stephen Boyd
Optimization course covering some other useful topics not covered in previous course.
This is a real advanced course on Machine Learning. Covering practically everything a general ML course can have(except really specialized topics).
5. Advanced Optimization and Randomized Methods by AlexSmola/SuvritSra at CMU
http://people.kyb.tuebingen.mpg.de/suvrit/teach/ee227a/lectures.html
Advanced optimization covering many new topics missing in basic optimization course. Many of these topics are useful in large scale optimization.
6. Introduction to robotics
7. Introduction to linear dynamical systems by Stephen Boyd

8. Probabilistic graphical models - advanced methods (Spring 2012) by murphy at stanford

9.  For inference and information theory(Mackay) [Lecture9-Lecture 14 Recommended]:
This is a course on information theory and inference. After first half the course practically changes to an ML course mostly inference and Neural Networks.
10. Machine Learning by nando freitas at UBC
11. Game Theory Part I and Part II at coursera
Help to build decision theory intuitions and concepts. Also one of the branches of AI.
12. Harvard Data Science
http://cm.dce.harvard.edu/2014/01/14328/publicationListing.shtml
13. Probabilistic Graphical Models 10-708, Spring 2014  Eric Xing (link)
14. Multiple View Geomtry by Prof. Prof. D. Cremers
15.  Variational  Methods for Computer Vision by Prof.  D. Cremers
16. Optimization by Geoff Gordan
17. Machine Learning by Aarti Singh and Geoff Gordon
18. Statistical Learning Theory Poggio (todo)
19. Statistical Machine Learning Larry Wasserman (todo)
20.  Regularization Methods for Machine Learning 2016
Shelf Books(pic from Stephen Gould):

ML Book list: (link to Michel Jordan recommendation)

 Standard ML Text Pattern Recognition and Machine Learning Christopher M. Bishop The Elements of Statistical Learning: Data Mining, Inference, and Prediction Trevor Hastie Machine Learning: A Probabilistic Perspective Kevin P. Murphy Probabilistic Graphical Models: Principles and Techniques Daphne Koller A Probabilistic Theory of Pattern Recognition Stochastic Modelling and Applied Probability Gabor Lugosi Probability Theory Probability and Random Processes Geoffrey R. Grimmett Probability Theory: The Logic of Science E.T. Jaynes Probability: Theory and Examples Richard Durrett A User's Guide to Measure Theoretic Probability David  Pollard Statistics All of Statistics: A Concise Course in Statistical Inference Larry Wasserman All of Nonparametric Statistics Larry Wasserman Statistical Inference Roger L. Berger Bayesian Theory and Practice Bayesian Core: A Practical Approach to Computational Bayesian Statistics Jean-Michel Marin The Bayesian Choice Christian P. Robert Bayesian Data Analysis Andrew Gelman Large Sample Theory and Asymptotic Statistics A Course in Large Sample Theory Thomas S. Ferguson Elements of Large-Sample Theory E.L. Lehmann Asymptotic Statistics A.W. van der Vaart Monte Carlo Statistical Methods Christian P. Robert Introduction to Nonparametric Estimation Alexandre B. Tsybakov Large-Scale Inference Bradley Efron Optimization Linear Algebra and Its Applications Gilbert Strang Matrix Computations Gene H. Golub Introduction to Linear Optimization Dimitris Bertsimas Numerical Optimization Jorge Nocedal Introductory Lectures on Convex Optimization: A Basic Course Y. Nesterov Convex Optimization Stephen Boyd Nonlinear Programming Dimitri P. Bertsekas Information Theory Elements of Information Theory Thomas M. Cover Information Theory, Inference and Learning Algorithms David J.C. MacKay Analysis Introductory Functional Analysis with Applications Erwin Kreyszig

Dont Look DOWN!!!  Will edit and organize stuff after this soon
________________________________________________________________________________

http://www.convexoptimization.com/dattorro/convex_optimization.html
http://www.convexoptimization.com/wikimization/index.php/

EE464: Semidefinite Optimization and Algebraic Techniques
Convex Analysis lecture notes by Nemirovski

http://www2.isye.gatech.edu/~nemirovs/OptIII_TR.pdf
http://www2.isye.gatech.edu/~nemirovs/OPTIII_LectureNotes.pdf
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-253-convex-analysis-and-optimization-spring-2012/lecture-notes/MIT6_253S12_lec_comp.pdf  (DIMITRI P. BERTSEKAS)

Probabilistic Models for Cognition by Noah Good Man and Joshua Tenenbaum

# http://www.brainshark.com/brainshark/brainshark.net/portal/title.aspx?pid=zCdz10BfTRz0z0#!

More ML books:

A lot of lectures related to AI by Coursera
https://www.coursera.org/category/cs-ai

A cool intro to machine learning with python examples
Programming Collective Intelligence: Building Smart Web 2.0 Applications by Toby Segaran

http://www.stanford.edu/~hastie/local.ftp/Springer/ESLII_print5.pdf

• Pattern Recognition and Machine Learning by Christopher Bishop
• Machine Learning by Tom Mitchell
• Machine Learning A Probabilistic Perspective by Kevin Murphy(I choose this)
some more resources on ML class resources link by Andrew Ng:
https://share.coursera.org/wiki/index.php/ML:Useful_Resources
• numerical optimization nocedal wright
http://www.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf
• Non-Linear Programming by Dimitri P Bertsekas

For Scalability of Machine Learning read

## by Ron Bekkerman, Mikhail Bilenko, John Langford

For Graphical Models

## Compressed Sensing

http://dsp.rice.edu/cs
http://nuit-blanche.blogspot.fr/

UFLDL
http://web.eecs.umich.edu/~honglak/teaching/eecs598/schedule.html
http://www.cs.toronto.edu/~hinton/deeprefs.html
http://deeplearning.stanford.edu/wiki/index.php/Main_Page