## Machine Learning

# Quick start guide(Video Links)

The following knowledge is prerequisite to make any sense out of Machine learning

**Linear Algebra by Gilbert Strang**: http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/**Convex Optimization by Boyd**http://see.stanford.edu/see/courseinfo.aspx?coll=2db7ced4-39d1-4fdb-90e8-364129597c87**Probability and statistics for ML**: http://videolectures.net/bootcamp07_keller_bss/**Some mathematical tools for ML**: http://videolectures.net/mlss03_burges_smtml/ Video+Audio Very bad quality**Probability primer**(measure theory and probability theory) : http://www.youtube.com/playlist?list=PL17567A1A3F5DB5E4&feature=plcp

Once the prerequisites are complete, the following are good series of lectures on Machine Learning.

### Basic ML:

**Andrew Ng’s Video Lectures(CS229)**: http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1**Andrew Ng’s online course offering**: http://www.ml-class.org- Learning from Data by Yaser Abu-Mostafa http://work.caltech.edu/telecourse.html
**Tom Mitchell’s video lectures(10-701)**: http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml**Mathematicalmonk’s videos**: http://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA&feature=plpp

### Advanced ML:

**SVMs and kernel methods**,**Scholkopf**: http://videolectures.net/mlss03_scholkopf_lk/

basics for Support Vector Machines and related Kernel methods. Video+Audio Very bad quality**Kernel methods and Support Vector Machines, Smola:**http://videolectures.net/mlss08au_smola_ksvm/

Introduction of the main ideas of statistical learning theory, Support Vector Machines, Kernel Feature Spaces, An overview of the applications of Kernel Methods.**Easily one of the best talks on SVM. Almost like a run-down tutorial.**http://videolectures.net/mlss06tw_lin_svm/**Introduction to Learning Theory, Olivier Bousquet**. http://videolectures.net/mlss06au_bousquet_ilt/

This tutorial focuses on the “larger picture” than on mathematical proofs, it is not restricted to statistical learning theory however. 5 lectures.**Statistical Learning Theory,**Olivier Bousquet, http://videolectures.net/mlss07_bousquet_slt/

This course gives a detailed introduction to Learning Theory with a focus on the Classification problem.**Statistical Learning Theory, John-Shawe Taylor**, University of London. 7 lectures. http://videolectures.net/mlss04_taylor_slt/**Advanced Statistical Learning Theory, Oliver Bousquet.**3 Lectures. http://videolectures.net/mlss04_bousquet_aslt/

Most of the above links have been filtered from http://onionesquereality.wordpress.com/2008/08/31/demystifying-support-vector-machines-for-beginners/

# Important Links:

- Channel for probability primer and Machine learning . : http://www.youtube.com/user/mathematicalmonk#grid/user/D0F06AA0D2E8FFBA [VIDEO]
- A comprehensive blog comprising of best resources for ML : http://onionesquereality.wordpress.com/2008/08/31/demystifying-support-vector-machines-for-beginners/ [links]
- Another great blog for ML http://www.quora.com/Machine-Learning/What-are-some-good-resources-for-learning-about-machine-learning-Why [links]
- Lectures 21-28 by Gilbert Strang, linear algebra way of optimization. http://academicearth.org/courses/mathematical-methods-for-engineers-ii

## 7 responses to “Machine Learning”

laxman

February 22nd, 2012 at 09:42

In which Indian institute (IITs/IISc or others) is research in this area the best ? I am talking about research in AI and any/all the underlying as in your diagram.

whiteswami

February 22nd, 2012 at 10:23

Firstly, AI is not very well pursued anywhere in Indian academia. The best AI institute is mit.csail and there are afew more US varsities. In India the scenario is at a very different platform. People have been doing nice work in ML, NLP, Games etc.. but not much has been done in AI.

Below are the institutes listed according to my prefernce(it might depend from person to person)…

Artificial Intelligence: IIT Madras, IIT Kharagpur

Machine Learning: IISc, (IIT Bombay, IIT Madras), IIT Kanpur

Game Theory : IISc

NLP : IIT Bombay, IIIT Hyd, IISc

Robotics : IIIT Allahabad

Genetic Algorithms: IIT Kanpur

Computer Vision: Not sure

..

..

Regards,

Rahul K Mishra

http://www.ee.iitb.ac.in/student/~rahulkmishra

Kedar U

March 22nd, 2013 at 11:08

Hi,

I have completed my B.E. and M.Tech in Computer Science and Engineering from BVBCET, Hubli, Now I would like to pursue research in Computer Science and Engg. from IITs or IISc but I am not getting which area to be chosen and how to prepare for the entrance test. Kindly give some suggestions.

Thanks in advance.

whiteswami

March 26th, 2013 at 19:05

You need to do an inside search on what interests you most. Rest will follow

kedar u

March 30th, 2013 at 10:38

Thank you for ur reply,

As we studied the subjects with theoretical approach there was not much practical exposure, so I am feeling it difficult to select the research area, if you could give any suggestions in this regard it will be helpful for me. Pls suggest some good research areas.

Madhavan Ramani

October 1st, 2013 at 10:27

Nice collection of Courses. I would like you to add “Learning from Data” by Prof. Yaser Abu-Mostafa (http://work.caltech.edu/telecourse.html). I have watched the initial lectures – his presentation seems easy to follow and the content is the same as the class that he teaches at CalTech.

whiteswami

October 3rd, 2013 at 10:37

My other blog mlthirst.wordpress.com already has this entry. Anyways, thanks for your input and the post has been updated.

BR,

Rahul

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