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Monday, May 14, 2012

Best machine learning Resources(for non-experts :P)

I am a beginner and I have spent some time on finding what are some of the good resources for ML.

LECTURES

ML from Coursera by Andrew Ng.(Sexy/awesome)
join the class when class starts next time(do advanced track) .
https://www.coursera.org/course/ml 

Lectures by Mathematical Monk on Probability, Machine learning, information theory(its good!!)
http://www.youtube.com/user/mathematicalmonk/videos?view=1&flow=grid

Andrew Ng machine learning lectures 
http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1
see lecture notes because sometimes lectures are not clearly explained but still is good for introductory ML.

Linear Algebra By Gilbert Strang
Video lectures at MIT OCW(I believe the best on internet)
http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/

Scalable Machine Learning by Alex Smola
http://alex.smola.org/teaching/berkeley2012/
http://www.youtube.com/playlist?list=PLOxR6w3fIHWzljtDh7jKSx_cuSxEOCayP

Stanford CS246: Mining Massive Data Sets
http://snap.stanford.edu/class/cs246-video-2013/
http://snap.stanford.edu/class/cs246-videos/

CS224W: Social and Information Network Analysis
http://snap.stanford.edu/class/cs224w-videos-2012/
http://snap.stanford.edu/class/cs224w-readings/
Big Data, Large Scale Machine Learning 

Convex Optimization I  by Stephen Boyd
http://see.stanford.edu/see/lecturelist.aspx?coll=2db7ced4-39d1-4fdb-90e8-364129597c87

Convex Optimization II  by Stephen Boyd
http://see.stanford.edu/see/courseinfo.aspx?coll=523bbab2-dcc1-4b5a-b78f-4c9dc8c7cf7a

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

EE464: Semidefinite Optimization and Algebraic Techniques

Advanced  Optimization cmu

Convex Analysis lecture notes by Nemirovski
http://www2.isye.gatech.edu/~nemirovs/
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)

Introduction to robotics 
http://see.stanford.edu/see/lecturelist.aspx?coll=86cc8662-f6e4-43c3-a1be-b30d1d179743

Introduction to linear dynamical systems by Stephen Boyd
http://see.stanford.edu/see/lecturelist.aspx?coll=17005383-19c6-49ed-9497-2ba8bfcfe5f6 

Probabilistic Graphical Models by Daphne Koller
http://www.pgm-class.org/
http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=ProbabilisticGraphicalModels
Programming assignments is time taking in here.  
https://sites.google.com/site/cs228tspring2012/   (by murphy at stanford) 


For inference and information theory(Mackay) [Lecture9-Lecture 14 Recommended]:
http://www.inference.phy.cam.ac.uk/itprnn_lectures/

Compressed sensing

http://www.sms.cam.ac.uk/collection/1117766/#!

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

More ML books: 
http://www.reddit.com/r/MachineLearning/comments/1jeawf/machine_learning_books/

CS281: Advanced Machine Learning 
http://www.seas.harvard.edu/courses/cs281/ (Harvard University, Fall 2011)
 
Neural Networks for Machine Learning by Geoffrey Hinton

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

The above picture shows books recommended by Stephen Gould.

If you want to start a read then read lecture notes by Andrew Ng. 
http://cs229.stanford.edu/materials.html

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

If you want to read a book on ML then read 
  • The Elements of Statistical Learning(good book freely downloadable)
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
For optimization read 
  • Convex Optimization by Stephen Boyd(good book freely downloadable)
http://www.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf
  • Non-Linear Programming by Dimitri P Bertsekas

For Scalability of Machine Learning read

  • Scaling Up Machine Learning: Parallel and Distributed Approaches 

by Ron Bekkerman, Mikhail Bilenko, John Langford

For Graphical Models

  • Probabilistic Graphical Models: Principles and Techniques by Daphne Koller 

More books:

http://www.reddit.com/r/MachineLearning/comments/1jeawf/machine_learning_books/

http://pindancing.blogspot.in/2010/01/learning-about-machine-learniing.html

A very strong ML community:  

http://metaoptimize.com/

http://metaoptimize.com/qa

 Andrew Moores slides http://www.autonlab.org/tutorials/

http://videolectures.net/mlss04_bishop_gmvm/     (graphical models and variational methods bishop)

http://videolectures.net/mlss06tw_wainwright_gmvmm/   (GMvariational methodsmessage passing)

http://www.cs.jhu.edu/~jason/tutorials/variational.html (High level explaination)

MATLAB tutorial: basic and advanced:

http://code.google.com/p/yagtom/

http://lukstafi.blogspot.de/2013/10/artificial-intelligence-university.html

__________________________________________________________________________________

Dont look down.. :P ML and stuff

ML and random stuff... :D

Compressed Sensing

https://sites.google.com/site/igorcarron2/cs
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

Tom Minka's page 

http://alumni.media.mit.edu/~tpminka/

http://www.stats.ox.ac.uk/~teh/teaching/npbayes.html#modernbnp

http://mlg.eng.cam.ac.uk/zoubin/course05/index.html

http://mlg.eng.cam.ac.uk/teaching/4f13/1213/  (Machine Learning 2013 cambridge / Murphy/PRML textbook)

Gatsby Machine Learning Qualifying Exam Topic List 

http://www.cs.princeton.edu/~blei/courses.html 

http://mlg.eng.cam.ac.uk/zoubin/p8-07/index.html (Image search and modelling)

Advanced Topics in Machine Learning ( subspace learning, manifold learning, subspace clustering, manifold clustering)

http://www.vision.jhu.edu/teaching/learning10/

Short Python writeup.

http://alumni.media.mit.edu/~tpminka/PLE/python/python.html

Hadoop in python  

http://www.michael-noll.com/tutorials/writing-an-hadoop-mapreduce-program-in-python/

http://blog.cloudera.com/blog/2013/01/a-guide-to-python-frameworks-for-hadoop/ 

graphics:

http://inst.eecs.berkeley.edu/~cs184/fa12/onlinelectures1.html

 http://www.youtube.com/user/raviramamoorthi/videos?view=1&flow=grid

https://graphics.stanford.edu/wikis/cs348b-11/Lectures#Goals 

Large scale ML and data mining: 

http://www.stanford.edu/class/cs224w/

http://www.stanford.edu/class/cs246/handouts.html

http://www.stanford.edu/group/mmds/

http://www.cs.cornell.edu/Courses/cs6784/2010sp/ 

 NETLOGO tutorial 

http://i-programmer.info/programming/other-languages/5613-getting-started-with-netlogo.html 

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