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.
Road map to EP (Minka)
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/
http://www2.isye.gatech.edu/~nemirovs/OptIII_TR.pdf
http://www2.isye.gatech.edu/~nemirovs/OPTIII_LectureNotes.pdf
http://ocw.mit.edu/courses/electricalengineeringandcomputerscience/6253convexanalysisandoptimizationspring2012/lecturenotes/MIT6_253S12_lec_comp.pdf (DIMITRI P. BERTSEKAS)
Probabilistic Models for Cognition by Noah Good Man and Joshua Tenenbaum
A lot of lectures related to AI by Coursera
https://www.coursera.org/category/csai
A cool intro to machine learning with python examples
Programming Collective Intelligence: Building Smart Web 2.0 Applications
If you want to read a book on ML then read
https://share.coursera.org/wiki/index.php/ML:Useful_Resources
For optimization read
For Scalability of Machine Learning read
For Graphical Models
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).
Prerequisites
 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 prerequisite to Machine Learning. You can always come back for lookups. Multivariable Calculus by MIT OCW
Courses at Coursera
https://www.coursera.org/course/m2o2c2
https://www.coursera.org/learn/calculus1
https://www.coursera.org/course/sequence  ProbabilityAnyone of the three should be good enough. You can always come back for lookups.
Probability Primer by Mathematical Monk at Youtube
Probabilistic Systems Analysis and Applied Probability by MIT OCW
Machine Learning Lectures:
Beginners
 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).  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.  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.  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++
 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.  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.  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.  Introduction to ML This and This by Alex Smola 10701 at CMU
This is a foundation course for PhD students.
 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.  Introduction to Recommender Systems
Not too difficult course but the whole course is on recommender Systems.  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)  Mining Massive Data Sets by Jure Leskovec at Coursera/Stanford
Deals with scalability issues of ML/Datamining.  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).  Deep Learning by Yann Lecunn
Deep Learning course dealing with practicality. Doing work on GPU etc.  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.  Deep Learning by Nando D. F. Oxford
https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/  Wow course on reinforcement learning by david silverhttps://www.youtube.com/playlist?list=PL5X3mDkKaJrL42i_jhE4Np6E2Ol62Ofa
 Wow course on approximate dynamic programming AKA Reinforcement Learning by Dimitri P. Bertsekas https://www.youtube.com/playlist?list=PLiCLbsFQNFAxOmVeqPhI5er1LGf2L9I4
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  Advanced Inference / Submodular functions course by Jeff Bilmes
https://www.youtube.com/channel/UCvPnLF7oUh4pm575fZcUxg/videos
 Big Data, Large Scale Machine Learning by Langford, Lecunn
Again a practical course for dealing with scalability, online learning etc.  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.  Convex Optimization II by Stephen Boyd
Optimization course covering some other useful topics not covered in previous course.  CS281: Advanced Machine Learning by Ryan Adams at Harvard
This is a real advanced course on Machine Learning. Covering practically everything a general ML course can have(except really specialized topics).  Advanced Optimization and Randomized Methods by AlexSmola/SuvritSra at CMU
https://www.youtube.com/playlist?list=PLjTcdlvIS6cjdA8WVXNIk56X_SjICxt0d
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.  Introduction to robotics
 Introduction to linear dynamical systems by Stephen Boyd
 Probabilistic graphical models  advanced methods (Spring 2012) by murphy at stanford
 For inference and information theory(Mackay) [Lecture9Lecture 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.  Machine Learning by nando freitas at UBC
 Game Theory Part I and Part II at coursera
Help to build decision theory intuitions and concepts. Also one of the branches of AI.  Harvard Data Science
http://cm.dce.harvard.edu/2014/01/14328/publicationListing.shtml  Probabilistic Graphical Models 10708, Spring 2014 Eric Xing (link)
 Multiple View Geomtry by Prof. Prof. D. Cremers
 Variational Methods for Computer Vision by Prof. D. Cremers
 Visual Navigation for Flying Robots TUM
 Machine Learning for Computer Vision TUM
 Optimization by Geoff Gordan
 Machine Learning by Aarti Singh and Geoff Gordon
 A collection of links for streaming algorithms and data structures
 6.895 Sketching, Streaming and Sublinear Space Algorithms
 Statistical Learning Theory Poggio (todo)
 Statistical Machine Learning Larry Wasserman (todo)
 Regularization Methods for Machine Learning 2016
Shelf Books(pic from Stephen Gould):
ML Book list: (link to Michel Jordan recommendation)
ML Book list: (link to Michel Jordan recommendation)

https://www.quora.com/Whataresomegoodresourcesforlearningaboutnumericalanalysis/answer/AlexKamil
I will add more soon!!!
I will add more soon!!!
Dont Look DOWN!!! Will edit and organize stuff after this soon
________________________________________________________________________________
Road map to EP (Minka)
http://www.convexoptimization.com/dattorro/convex_optimization.html
http://www.convexoptimization.com/wikimization/index.php/
EE464: Semidefinite Optimization and Algebraic Techniques
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/electricalengineeringandcomputerscience/6253convexanalysisandoptimizationspring2012/lecturenotes/MIT6_253S12_lec_comp.pdf (DIMITRI P. BERTSEKAS)
Probabilistic Models for Cognition by Noah Good Man and Joshua Tenenbaum
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/
https://www.coursera.org/category/csai
A cool intro to machine learning with python examples
Programming Collective Intelligence: Building Smart Web 2.0 Applications
If you want to read a book on ML then read
 The Elements of Statistical Learning(good book freely downloadable)
 Pattern Recognition and Machine Learning by Christopher Bishop
 Machine Learning by Tom Mitchell
 Machine Learning A Probabilistic Perspective by Kevin Murphy(I choose this)
https://share.coursera.org/wiki/index.php/ML:Useful_Resources
For optimization read
 Convex Optimization by Stephen Boyd(good book freely downloadable)
 numerical optimization nocedal wright
 NonLinear Programming by
For Scalability of Machine Learning read
Scaling Up Machine Learning: Parallel and Distributed Approaches
Probabilistic Graphical Models: Principles and Techniques
https://sites.google.com/site/igorcarron2/cs
http://dsp.rice.edu/cs
http://nuitblanche.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
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
Best Organized resource !!! Thanks
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ReplyDeletehow much time you think should it take for a student in 3rd year to complete all of this machine learning or at least get an intermediate level enough to have a overview of every technique.
ReplyDeleteI consider myself an intermediate (or slightly above that) and 3 years is a good estimate if you are not into machine learning full time but lot of schools have advanced courses which can give you a head start.
DeleteI would also say that you should slow down you learning as retention is also an aspect of learning. Revisit if required. This is not a race and if you are passionate enough you will learn a lot in sometime.
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