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{ Online learning lecture notes}
[Bartok_online_learning.pdf Online Learning Lecture Notes]
Gabor Bartok, David Pal, Csaba Szepesvari, Istvan Szita.
[http://arxiv.org/pdf/1204.5721v2.pdf Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems]
Sebastien Bubeck and Nicolas Cesa-Bianchi.
[http://stat.wharton.upenn.edu/~rakhlin/book_draft.pdf Statistical Learning and Sequential Prediction]
Alexander Rakhlin and Karthik Sridharan.
[http://www-stat.wharton.upenn.edu/~rakhlin/papers/online_learning.pdf Lecture Notes on Online Learning]
Alexander Rakhlin.
[http://ocobook.cs.princeton.edu/OCObook.pdf Introduction to Online Convex Optimization]
Elad Hazan.
[http://www.cs.huji.ac.il/~shais/papers/OLsurvey.pdf Online Learning and Online Convex Optimization]
Shai Shalev-Shwartz.
[http://banditalgs.com/ Bandit Algorithms] Tor Lattimore and Csaba Szepesvari.
[http://dept.stat.lsa.umich.edu/~tewaria/teaching/STATS710-Fall2016/ STATS 710: Sequential Decision Making with mHealth Applications] Susan Muephy and Ambuj Tewari.
[http://slivkins.com/work/MAB-book-Jan17.pdf Introduction to Multi-Armed Bandits] Aleksandrs Slivkins
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{ Reinforcement learning lecture notes}
[http://researchers.lille.inria.fr/~lazaric/Webpage/MVA-RL_Course14.html INRIA Cource]
Alessandro Lazaric.
[http://www.cs.cmu.edu/~ebrun/15889e/schedule.html CMU course]
Emma Brunskill.
[https://math.la.asu.edu/~jtaylor/teaching/Fall2012/STP425/lectures/MDP.pdf Markov Decision Processes: Lecture Notes]
Jay Taylor.
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{ Optimization lecture notes}
[http://arxiv.org/pdf/1405.4980v2.pdf Convex Optimization: Algorithms and Complexity]
Sebastien Bubeck.
[http://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf Convex Optimization]
Stephen Boyd and Lieven Vandenberghe.
[http://arxiv.org/pdf/1407.1537v4.pdf Linear Coupling: An Ultimate Unification of Gradient and Mirror Decent]
Zeyuan Allen-Zhu and Lorenzo Orecchia.
[https://arxiv.org/pdf/1606.04838.pdf Optimization Methods for Large-Scale Machine Learning]
Léon Bottou, Frank E. Curtis, Jorge Nocedal.
[./papers/convex_analysis.pdf Convex Analysis Backgrounds]
John Duchi.
[./papers/subgradient_methods.pdf Subgradient Methods]
John Duchi.
[./papers/Duchi16.pdf Introduction to Stochastic Optimization]
John Duchi.
Convex Analysis.
Tyrrell Rockafellar.
[http://theory.epfl.ch/vishnoi/Nisheeth-VishnoiFall2014-ConvexOptimization.pdf A Mini-Course on Convex Optimization] Nisheeth K. Vishnoi
[./papers/Optimization_notes.pdf A lecture note on optimization] SVN Vishwanathan
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{Algorithms, theoretical computer science}
[http://www.cs.cornell.edu/jeh/bookMay2015.pdf Foundations of Data Science]
Avrim Blum, John Hopcroft, and Ravindran Kannan.
[http://www.parallel-algorithms-book.com/ Algorithm Design: Parallel and Sequential]
Guy Blelloch.
[http://www.designofapproxalgs.com/ The Design of Approximation Algorithms]
David P. Williamson and David B. Shmoys.
[http://jeffe.cs.illinois.edu/teaching/algorithms/all-models.pdf Models of Computation]
Jeff Erickson.
[http://jeffe.cs.illinois.edu/teaching/algorithms/all-algorithms.pdf Algorithms]
Jeff Erickson.
[http://www.cs.yale.edu/homes/aspnes/classes/469/notes.pdf Notes on Randomized Algorithms]
James Aspnes.
[http://www.cs.yale.edu/homes/aspnes/classes/202/notes.pdf Notes on Discrete Mathematics]
James Aspnes.
[http://mfleck.cs.illinois.edu/building-blocks/version-1.3/whole-book.pdf Building Blocks for Theoretical Computer Science]
Margaret M. Fleck.
[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.110.9927&rep=rep1&type=pdf Geometric approximation algorithms]
Sariel Har-Peled.
[http://cs-www.cs.yale.edu/homes/aspnes/classes/468/notes.pdf Computational Complexity]
James Aspnes
[https://people.eecs.berkeley.edu/~luca/notes/complexitynotes02.pdf Lecture Notes on Computational Complexity] [https://lucatrevisan.wordpress.com Luca Trevisan]
[http://www.introtcs.org/public/lnotes_book.pdf Intro to Theoretical Computer Science]
Boaz Barak
[https://www.cs.cmu.edu/afs/cs.cmu.edu/academic/class/15859-f11/www/notes/lpsdp.pdf Lecture notes for CMU's course on Linear Programming and Semidefinite Programming]
Anupam Gupta and Ryan O’Donnell
[https://homes.cs.washington.edu/~karlin/GameTheoryBook.pdf Game Theory, Alive!]
Anna R. Karlin and Yuval Peres
[https://courses.csail.mit.edu/6.042/spring17/mcs.pdf Mathematics for Computer Science]
Eric Lehman, Thomson Leighton and Albert Meyer
[https://www.ma.utexas.edu/users/ntran/probcombi.pdf Probabilistic Combinatoris]
Ngoc Mai Tran
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{Learning Theory}
[http://www.econ.upf.edu/~lugosi/esaimsurvey.pdf Theory of Classification]
Stephane Boucheron, Olivier Bousquet and Gabor Lugosi.
[slt.pdf Introduction to Statistical Learning Theory]
Stephane Boucheron, Olivier Bousquet and Gabor Lugosi
[http://www-math.mit.edu/~rigollet/courses/notes.html Mathematics of Machine Learning]
Philippe Regollet.
[http://people.csail.mit.edu/moitra/docs/bookex.pdf Algorithmic Aspects of Machine Learning ]
Ankur Moitra.
[http://www.cs.cmu.edu/~ninamf/courses/806/10-806-index.html Some PAC / Some active learning lecture notes]
Nina Balcan.
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{Statistics \/ Random Processes \/ Concentration Inequalities}
[http://sites.stat.psu.edu/~dhunter/asymp/lectures/asymp.pdf Notes for a graduate-level course inasymptotics for statisticians]
David R. Hunter
[http://www.tau.ac.il/~mansour/advanced-agt+ml/scribe5-lower-bound-MAB.pdf Lower Bounds using Information Theory Tools]
Yishay Mansour.
[http://www-personal.umich.edu/~romanv/teaching/2007-08/235B/lecture-notes.pdf Probability Theory]
Lecture notes of R. Vershynin's class.
[https://www.math.uci.edu/~rvershyn/papers/HDP-book/HDP-book.pdf High-Dimensional Probability]
Roman Vershynin.
[http://www.cs.cornell.edu/~sridharan/concentration.pdf A Gentle Introduction to Concentration Inequalities]
Karthik Sridharan.
[http://stats.stackexchange.com/questions/21362/understanding-proof-of-mcdiarmids-inequality McDiarmids Inequality]
[http://stanford.edu/class/stats311/Lectures/full_notes.pdf Information Theory and Statistics]
John Duchi
[http://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf Advanced Data Analysis from an Elementary Point of View]
Cosma Rohilla Shalizi
[http://www.princeton.edu/~rvan/ORF570.pdf Probability in High Dimension ]
Ramon van Handel.
[http://www-math.mit.edu/~rigollet/PDFs/RigNotes17.pdf High Dimensional Statistics]
Philippe Rigollet.
[https://services.math.duke.edu/~rtd/PTE/PTE4_1.pdf Probability: Theory and Examples]
Rick Durrett
[http://www.math.ucsd.edu/~fan/wp/concen.pdf Concentration Inequalities and Martingale Inequalities: A Survey ]
Fan Chung and Linyuan Lu.
[http://www.springer.com/us/book/9783540484974 (Springer) Concentration Inequalities and Model Selection]
Pascal Massart.
[http://arxiv.org/pdf/1501.01571v1.pdf An Introduction to Matrix Concentration Inequalities.]
Joel Tropp.
[http://www-personal.umich.edu/~romanv/papers/non-asymptotic-rmt-plain.pdf Introduction to the non-asymptotic analysis of random matrices]
Roman Vershynin.
[http://www.stat.math.ethz.ch/~geer/mathstat.pdf Mathematical Statistics]
Sara van de Geer.
[http://www.stat.math.ethz.ch/~geer/empirical-processes.pdf Empirical Processes]
Sara van de Geer.
[http://www.stat.cmu.edu/~larry/=stat705/ Intermediate Statistics]
Larry Wasserman.
[http://www.stat.cmu.edu/~larry/=sml/ Statistical Machine Learning]
Larry Wasserman.
[http://www.ifp.illinois.edu/~hajek/Papers/randomprocJuly14.pdf Random Processes for Engineers]
Bruce Hajek.
[https://courses.engr.illinois.edu/ece313/su2015/probabilityJune15.pdf Probability with Engineering Applications]
Bruce Hajek.
[http://ee.stanford.edu/~gray/sp.pdf An Introduction to statistical signal processing]
Robert Gray and Lee Davisson.
[http://people.inf.ethz.ch/arbenz/ewp/Lnotes/lsevp.pdf Lecture Notes on
Solving Large Scale Eigenvalue Problems]
Peter Arbenz
[http://www.stat.ucla.edu/~arash.amini/teaching/stat200c/notes/matrix_norms.pdf Notes on matrix norms]
Arash A. Amini
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{Programming}
[http://www.cs.yale.edu/homes/aspnes/classes/223/notes.html Notes on Data Structures and Programming Techniques ]
James Aspnes.
[http://opendatastructures.org/ods-python-screen.pdf Open Data Structures (in pseudocode)]
Pat Morin.
[https://progit.org/ Pro Git]
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{Others}
[./pcml_notes.pdf Pattern Classification and Machine Learning]
Matthias Seeger.
[http://arxiv.org/pdf/0904.3664v1.pdf Introduction to Machine Learning]
Amnon Shashua.
[https://goodfeli.github.io/dlbook/ Deep Learning]
Ian Goodfellow, Aaron Courville, and Yoshua Bengio.
[http://www.cs.columbia.edu/~blei/fogm/2015F/ Graphical Models (Princeton) ]
David Blei.
[http://www.di.ens.fr/~slacoste/teaching/MVA_GM/fall2015/ Graphical Models (INRIA)]
Guillaume Obozinski, Simon Lacoste-Julien, and Francis Bach.
[http://www.cs.cmu.edu/~epxing/Class/10708/lecture.html Graphical Models (CMU)]
Eric Xing.
Science Research Writing for Non-Native Speakers of English. Hilary Glasman-Deal.
[http://jmlr.csail.mit.edu/reviewing-papers/knuth_mathematical_writing.pdf Mathematical Writing] Donald Knuth.
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{Recommended MOOC courses}
[https://www.udacity.com/wiki/cs212 Design of Computer Programs (Udacity)]
Peter Norvig.
[https://www.udacity.com/course/reinforcement-learning--ud600 Reinforcement Learning (Udacity)]
Michael Littman and Charles Isbell.
[https://www.coursera.org/course/algo Algorithms: Design and Analysis, Part 1 and Part 2 (coursera)]
Tim Roughgarden
[https://www.coursera.org/course/nlangp Natural Language Processing (coursera)]
Michael Collins
[http://j.ee.washington.edu/~bilmes/classes/ee512a_fall_2014/ Advanced Inference in Graphical Models (youtube)]
Jeff Bilmes
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{Bayesian Modeling}
[http://www.math.uconn.edu/~kconrad/blurbs/analysis/entropypost.pdf Probability distribution and maximum entropy]
Keith Conrad.
[http://arxiv.org/pdf/1412.7392.pdf Theoretical guarantees for approximate sampling from smooth and log-concave densities]
Arnak S. Dalalyan.
[http://www.lx.it.pt/~mtf/learning/Bayes_lecture_notes.pdf Lecture Notes on Bayesian Estimation and Classification]
Mario A. T. Figueired.
[http://www.cs.ubc.ca/~murphyk/Papers/bayesGauss.pdf Conjugate Bayesian analysis of the Gaussian distribution]
Kevin Murphy.
[http://www.cs.ubc.ca/~murphyk/Papers/learncg.pdf Fitting a Conditional Linear Gaussian Distribution]
Kevin Murphy
[https://www.umiacs.umd.edu/~resnik/pubs/LAMP-TR-153.pdf Gibbs Sampling for the uninitiated]
Philip Resnik and Eric Hardisty
[http://www.icml-2011.org/papers/398_icmlpaper.pdf Bayesian Learning via Stochastic Gradient Langevin Dynamics]
Max Welling and Yee Whye Teh.
[http://www.ics.uci.edu/~welling/classnotes/classnotes.html Bayesian Machine Learning lecture notes]
Max Welling.
[http://pages.uoregon.edu/dlevin/MARKOV/ Markov Chains and Mixing Times]
David Levin, Yuval Peres, and Elizabeth Wilmer.
[http://stat.columbia.edu/~porbanz/papers/porbanz_BNP_draft.pdf Lecture Notes on Bayesian Nonparametrics]
Peter Orbanz.
[http://www.springer.com/br/book/9780387733937 (Springer) An Introduction to Bayesian Scientific Computing: Ten Lectures on Subjective Computing]
Daniela Calvetti and Erkki Somersalo.
[http://www.springer.com/gp/book/9780387922997 (Springer) A First Course in Bayesian Statistical Methods.]
Peter D. Hoff.
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