I was seeing Andrew Ng's ML lectures and I was half way.
Then I was surprised when I saw a link where there were some prerequisites suggested by someone which I never thought were prerequisites for Basic ML.
All were good links(the prerequisite list were the ones I knew) which i wanted to complete after ANg's lecture.
I would like to post them here for people who want to give time for ML and for my own reference.
I myself have not completed the prerequisites but I am sure that all of them are good(everyone suggests the same).
Quick start guide(Video Links)
People generally ignore this but knowing about Algorithms of CS is also important.(CORMEN/Skiena/Kleinberg)
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
Learning from data by CalTech: http://work.caltech.edu/telecourse.html
Advanced ML:
Probabilistic graphical models by Daphne Koller(Stanford)
http://www.pgm-class.org/
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
Then I was surprised when I saw a link where there were some prerequisites suggested by someone which I never thought were prerequisites for Basic ML.
All were good links(the prerequisite list were the ones I knew) which i wanted to complete after ANg's lecture.
I would like to post them here for people who want to give time for ML and for my own reference.
I myself have not completed the prerequisites but I am sure that all of them are good(everyone suggests the same).
Quick start guide(Video Links)
People generally ignore this but knowing about Algorithms of CS is also important.(CORMEN/Skiena/Kleinberg)
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
Learning from data by CalTech: http://work.caltech.edu/telecourse.html
Advanced ML:
Probabilistic graphical models by Daphne Koller(Stanford)
http://www.pgm-class.org/
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
