# jemdoc: menu{MENU}{lr.html}, showsource ~~~ { 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 ~~~ ~~~ { 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. ~~~ ~~~ { 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 ~~~ ~~~ {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 ~~~ ~~~ {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. ~~~ ~~~ {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 ~~~ ~~~ {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] ~~~ ~~~ {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. ~~~ ~~~ {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 ~~~ ~~~ {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. ~~~