Machine Learning

Figure: ML Dependency and interplay

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
• 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