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This is the place where all the knowledge in Computer Science is accumulated..

Machine Learning

Learning Theory

Computational Learning Theory - Varun Kanade 2018
Machine Learning Theory - Akshay Krishnamurthy 2017
Machine Learning Theory (contains links to other courses) - Matus Telgarski 2018
Machine Learning Theory - Karthik Sridharan
Statistical Learning Theory - Shivani Agarwal 2011
Theoretical Machine Learning - Robert Shapire 2019
Topics in Artificial Intelligence (Learning Theory) - Ambuj Tewari 2008
Computational and Statistical Learning Theory - Nati Srebro
Introduction to Computational Learning Theory (only handwritten notes) - Rocco Servedio 2018
Machine Learning Theory - Jake Abernethy 2019
Advanced Learning Theory - Ilias Diakonikolas 2019
Robustness in Machine Learning - Jerry Li 2019
Robust Statistic - Jacob Steinhardt 2019
The Algorithmic Foundations of Adaptive Data Analysis - Aaron Roth 2017

Online Learning

Introduction to Online Learning - Haipeng Luo 2017
Online Methods in Machine Learning, Theory and Applications - Sasha Rakhlin
Online Learning - Brendan McMahan 2014
Advanced Topics in ML and AG - Mansour 2018
Online and Adaptive Methods for Machine Learning - Jamieson 2018
Slivkins - Advanced Topics in Theory of Computing: Bandits, Experts, and Games 2016
Introduction to Online Learning - Orabona 2019

Reinforcement Learning

Foundations of Reinforcement Learning - Chi Jin (contains links to other courses)

Computer Science

Toolkit

Great Ideas in Theoretical Computer Science - CMU 2018
Mathematical toolkit - Madhur Tulsiani 2018
Topics in Theoretical Computer Science: An Algorithmist's Toolkit - Kelner 2014
Efficient Algorithms and Intractable Problems - Alessandro Chiesa & Jelani Nelson 2020
Algorithms - Avrim Blum 2019
Advanced Algorithms (and data structures) - Jelani Nelson 2017
Advanced algorithm design - Sanjeev Arora 2014
TCS Toolkit - Ryan O'Donnell (with videos on Youtube) 2020

Big Data & Sublinear Time Algorithms

Randomized Algorithms

[Randomized algorithms and probabilistic analysis](https://homes.cs.washington.edu/~jrl/teaching/cse525au16/index.htmlRandomized Algorithms) - James R. Lee 2016
Randomized Algorithms - Sariel Har-Peled 2014
Randomness and computation - Lap Chi Lau
Randomized Algorithms - Eric Price 2016

Graph and Spectral Algorithms

Spectral algorithms - Georgia Tech
Spectral Graph Theory, Spielman 2018 (there are various edition of this course on his page)
Spectral Graph Theory and Algorithmic Applications - Amin Saberi
Spectral Graph Theory - David P. Williamson (contains links to other courses)
Spectral Graph Theory and the Laplacian Paradigm - Gary Miller 2018
Graph algorithms - Virginia Vassilevska Williams 2016
Algorithms for Graphs and Matrices - Virginia Vassilevska Williams
Graphs, Linear Algebra, and Optimization - Aleksander Mądry 2015
Graph algorithms - Debmalya Panigrahi 2017
Sparse Approximations - Nick Harvey 2012
Iterative methods for graph algorithm and network analysis - Lorenzo Orecchia 2018

Online and Approximation Algorithms

Online and Approximation Algorithms - Susanne Albers 2017
Algorithms and Uncertainty - Nikhil Bansal 2016
Efficient Algorithms and Data Structures II - Harald Racke 2019
Approximation Algorithms - Michael Dinitz 2019
Approximation Algorithms and Hardness of Approximation - Ola Svensson (maybe 2013)
Approximation Algorithms - Yuval Rabani
Advanced Approximation Algorithms - Anupam Gupta and Ryan O'Donnell 2008
Approximation Algorithms - Debmalya Panigrahi 2017
Optimization and Algorithmic paradigms - Luca Trevisan 2011
Approximation Algorithms - Shuchi Chawla 2007
Recent Advances in Approximation Algorithms - Shayan Oveis Gharan 2015

Algorithmic Game Theory

Algorithmic Game Theory and Data Science - Constantinos Daskalakis & Vasilis Syrgkanis 2019
Learning, Games, and Electronic Markets - Robert Kleinberg 2007
Algorithms, Games, and Networks - Ariel Procaccia & Avrim Blum 2013
Algorithmic Game Theory - Tim Roughgarden 2013
Economics, AI, and Optimization - Christian Kroer 2020
Algorithmic Game Theory - book by Nisan, Roughgarden, Tardos, Vazirani

Mathematical Programming

Convex Optimization

Convexity and Optimization - Lap Chi Lau
Algorithms for Convex Optimization - Nisheet Vishnoi
A Mini-Course on Convex Optimization - Notes by Vishnoi
Introduction to Optimization Theory - Aaron Sidford 2017
Convex Optimization I - Stephen Boyd & John Duchi
Convex Optimization II - John Duchi
Convex Optimization - Pradeep Ravikumar & Aarti Singh 2017
Convex Optimization and Approximation - Moritz Hardt 2018
Optimization for Machine Learning - Martin Jaggi
Convex Optimization - Ryan Tibshirani 2018
Optimization Methods in Statistics - Ambuj Tewari 2015 (with link to other courses)
Cheat Sheet - series of posts - Sebastian Pokutta
Advanced Structured Prediction and Optimization - Simon Lacoste-Julien 2020