Online Learning Lecture Notes Gabor Bartok, David Pal, Csaba Szepesvari, Istvan Szita.

Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems Sebastien Bubeck and Nicolas Cesa-Bianchi.

Statistical Learning and Sequential Prediction Alexander Rakhlin and Karthik Sridharan.

Lecture Notes on Online Learning Alexander Rakhlin.

Introduction to Online Convex Optimization Elad Hazan.

Online Learning and Online Convex Optimization Shai Shalev-Shwartz.

Bandit Algorithms Tor Lattimore and Csaba Szepesvari.

STATS 710: Sequential Decision Making with mHealth Applications Susan Muephy and Ambuj Tewari.

Introduction to Multi-Armed Bandits Aleksandrs Slivkins

INRIA Cource Alessandro Lazaric.

CMU course Emma Brunskill.

Markov Decision Processes: Lecture Notes Jay Taylor.

Convex Optimization: Algorithms and Complexity Sebastien Bubeck.

Convex Optimization Stephen Boyd and Lieven Vandenberghe.

Linear Coupling: An Ultimate Unification of Gradient and Mirror Decent Zeyuan Allen-Zhu and Lorenzo Orecchia.

Optimization Methods for Large-Scale Machine Learning Léon Bottou, Frank E. Curtis, Jorge Nocedal.

Convex Analysis Backgrounds John Duchi.

Subgradient Methods John Duchi.

Introduction to Stochastic Optimization John Duchi.

Convex Analysis. Tyrrell Rockafellar.

A Mini-Course on Convex Optimization Nisheeth K. Vishnoi

A lecture note on optimization SVN Vishwanathan

Foundations of Data Science Avrim Blum, John Hopcroft, and Ravindran Kannan.

Algorithm Design: Parallel and Sequential Guy Blelloch.

The Design of Approximation Algorithms David P. Williamson and David B. Shmoys.

Models of Computation Jeff Erickson.

Algorithms Jeff Erickson.

Notes on Randomized Algorithms James Aspnes.

Notes on Discrete Mathematics James Aspnes.

Building Blocks for Theoretical Computer Science Margaret M. Fleck.

Geometric approximation algorithms Sariel Har-Peled.

Computational Complexity James Aspnes

Lecture Notes on Computational Complexity Luca Trevisan

Intro to Theoretical Computer Science Boaz Barak

Lecture notes for CMU's course on Linear Programming and Semidefinite Programming Anupam Gupta and Ryan O’Donnell

Game Theory, Alive! Anna R. Karlin and Yuval Peres

Mathematics for Computer Science Eric Lehman, Thomson Leighton and Albert Meyer

Probabilistic Combinatoris Ngoc Mai Tran

Theory of Classification Stephane Boucheron, Olivier Bousquet and Gabor Lugosi.

Introduction to Statistical Learning Theory Stephane Boucheron, Olivier Bousquet and Gabor Lugosi

Mathematics of Machine Learning Philippe Regollet.

Algorithmic Aspects of Machine Learning Ankur Moitra.

Some PAC / Some active learning lecture notes Nina Balcan.

Notes for a graduate-level course inasymptotics for statisticians David R. Hunter

Lower Bounds using Information Theory Tools Yishay Mansour.

Probability Theory Lecture notes of R. Vershynin's class.

High-Dimensional Probability Roman Vershynin.

A Gentle Introduction to Concentration Inequalities Karthik Sridharan.

McDiarmids Inequality

Information Theory and Statistics John Duchi

Advanced Data Analysis from an Elementary Point of View Cosma Rohilla Shalizi

Probability in High Dimension Ramon van Handel.

High Dimensional Statistics Philippe Rigollet.

Probability: Theory and Examples Rick Durrett

Concentration Inequalities and Martingale Inequalities: A Survey Fan Chung and Linyuan Lu.

(Springer) Concentration Inequalities and Model Selection Pascal Massart.

An Introduction to Matrix Concentration Inequalities. Joel Tropp.

Introduction to the non-asymptotic analysis of random matrices Roman Vershynin.

Mathematical Statistics Sara van de Geer.

Empirical Processes Sara van de Geer.

Intermediate Statistics Larry Wasserman.

Statistical Machine Learning Larry Wasserman.

Random Processes for Engineers Bruce Hajek.

Probability with Engineering Applications Bruce Hajek.

An Introduction to statistical signal processing Robert Gray and Lee Davisson.

Lecture Notes on Solving Large Scale Eigenvalue Problems Peter Arbenz

Notes on matrix norms Arash A. Amini

Notes on Data Structures and Programming Techniques James Aspnes.

Open Data Structures (in pseudocode) Pat Morin.

Pro Git

Pattern Classification and Machine Learning Matthias Seeger.

Introduction to Machine Learning Amnon Shashua.

Deep Learning Ian Goodfellow, Aaron Courville, and Yoshua Bengio.

Graphical Models (Princeton) David Blei.

Graphical Models (INRIA) Guillaume Obozinski, Simon Lacoste-Julien, and Francis Bach.

Graphical Models (CMU) Eric Xing.

Science Research Writing for Non-Native Speakers of English. Hilary Glasman-Deal.

Mathematical Writing Donald Knuth.

Design of Computer Programs (Udacity) Peter Norvig.

Reinforcement Learning (Udacity) Michael Littman and Charles Isbell.

Algorithms: Design and Analysis, Part 1 and Part 2 (coursera) Tim Roughgarden

Natural Language Processing (coursera) Michael Collins

Advanced Inference in Graphical Models (youtube) Jeff Bilmes

Probability distribution and maximum entropy Keith Conrad.

Theoretical guarantees for approximate sampling from smooth and log-concave densities Arnak S. Dalalyan.

Lecture Notes on Bayesian Estimation and Classification Mario A. T. Figueired.

Conjugate Bayesian analysis of the Gaussian distribution Kevin Murphy.

Fitting a Conditional Linear Gaussian Distribution Kevin Murphy

Gibbs Sampling for the uninitiated Philip Resnik and Eric Hardisty

Bayesian Learning via Stochastic Gradient Langevin Dynamics Max Welling and Yee Whye Teh.

Bayesian Machine Learning lecture notes Max Welling.

Markov Chains and Mixing Times David Levin, Yuval Peres, and Elizabeth Wilmer.

Lecture Notes on Bayesian Nonparametrics Peter Orbanz.

(Springer) An Introduction to Bayesian Scientific Computing: Ten Lectures on Subjective Computing Daniela Calvetti and Erkki Somersalo.

(Springer) A First Course in Bayesian Statistical Methods. Peter D. Hoff.