This is the place where all the knowledge in Computer Science is accumulated..
Machine Learning
Learning Theory
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Computational Learning Theory - Varun Kanade 2018
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Machine Learning Theory - Akshay Krishnamurthy 2017
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Machine Learning Theory (contains links to other courses) - Matus Telgarski 2018
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Machine Learning Theory - Karthik Sridharan
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Statistical Learning Theory - Shivani Agarwal 2011
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Theoretical Machine Learning - Robert Shapire 2019
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Topics in Artificial Intelligence (Learning Theory) - Ambuj Tewari 2008
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Computational and Statistical Learning Theory - Nati Srebro
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Introduction to Computational Learning Theory (only handwritten notes) - Rocco Servedio 2018
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Machine Learning Theory - Jake Abernethy 2019
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Advanced Learning Theory - Ilias Diakonikolas 2019
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Robustness in Machine Learning - Jerry Li 2019
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Robust Statistic - Jacob Steinhardt 2019
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The Algorithmic Foundations of Adaptive Data Analysis - Aaron Roth 2017
Online Learning
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Introduction to Online Learning - Haipeng Luo 2017
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Online Methods in Machine Learning, Theory and Applications - Sasha Rakhlin
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Online Learning - Brendan McMahan 2014
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Advanced Topics in ML and AG - Mansour 2018
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Online and Adaptive Methods for Machine Learning - Jamieson 2018
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Slivkins - Advanced Topics in Theory of Computing: Bandits, Experts, and Games 2016
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Introduction to Online Learning - Orabona 2019
Reinforcement Learning
- Foundations of Reinforcement Learning - Chi Jin (contains links to other courses)
Computer Science
Toolkit
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Great Ideas in Theoretical Computer Science - CMU 2018
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Mathematical toolkit - Madhur Tulsiani 2018
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Topics in Theoretical Computer Science: An Algorithmist’s Toolkit - Kelner 2014
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Efficient Algorithms and Intractable Problems - Alessandro Chiesa & Jelani Nelson 2020
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Algorithms - Avrim Blum 2019
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Advanced Algorithms (and data structures) - Jelani Nelson 2017
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Advanced algorithm design - Sanjeev Arora 2014
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TCS Toolkit - Ryan O’Donnell (with videos on Youtube) 2020
Big Data & Sublinear Time Algorithms
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Woodruff - contains link to other courses
Randomized Algorithms
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Randomized algorithms and probabilistic analysis - James R. Lee 2016
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Randomized Algorithms - Sariel Har-Peled 2014
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Randomness and computation - Lap Chi Lau
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Randomized Algorithms - Eric Price 2016
Graph and Spectral Algorithms
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Spectral algorithms - Georgia Tech
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Spectral Graph Theory, Spielman 2018 (there are various edition of this course on his page)
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Spectral Graph Theory and Algorithmic Applications - Amin Saberi
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Spectral Graph Theory - David P. Williamson (contains links to other courses)
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Spectral Graph Theory and the Laplacian Paradigm - Gary Miller 2018
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Graph algorithms - Virginia Vassilevska Williams 2016
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Algorithms for Graphs and Matrices - Virginia Vassilevska Williams
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Graphs, Linear Algebra, and Optimization - Aleksander Mądry 2015
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Graph algorithms - Debmalya Panigrahi 2017
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Sparse Approximations - Nick Harvey 2012
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Iterative methods for graph algorithm and network analysis - Lorenzo Orecchia 2018
Online and Approximation Algorithms
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Online and Approximation Algorithms - Susanne Albers 2017
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Algorithms and Uncertainty - Nikhil Bansal 2016
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Efficient Algorithms and Data Structures II - Harald Racke 2019
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Approximation Algorithms - Michael Dinitz 2019
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Approximation Algorithms and Hardness of Approximation - Ola Svensson (maybe 2013)
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Approximation Algorithms - Yuval Rabani
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Advanced Approximation Algorithms - Anupam Gupta and Ryan O’Donnell 2008
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Approximation Algorithms - Debmalya Panigrahi 2017
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Optimization and Algorithmic paradigms - Luca Trevisan 2011
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Approximation Algorithms - Shuchi Chawla 2007
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Recent Advances in Approximation Algorithms - Shayan Oveis Gharan 2015
Algorithmic Game Theory
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Algorithmic Game Theory and Data Science - Constantinos Daskalakis & Vasilis Syrgkanis 2019
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Learning, Games, and Electronic Markets - Robert Kleinberg 2007
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Algorithms, Games, and Networks - Ariel Procaccia & Avrim Blum 2013
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Algorithmic Game Theory - Tim Roughgarden 2013
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Economics, AI, and Optimization - Christian Kroer 2020
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Algorithmic Game Theory - book by Nisan, Roughgarden, Tardos, Vazirani
Optimization
Mathematical Programming
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Mathematical Programming - David Shmoys (link to lecture 25)
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Polyhedral Techniques in Combinatorial Optimization Stanford - Jan Vondrak 2017
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Linear and SemiDefinite Programming - Gupta & O’Donnell
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Algebraic Techniques and semidefinite programming - Pablo Parrilo
Convex Optimization
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Convexity and Optimization - Lap Chi Lau
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Algorithms for Convex Optimization - Nisheet Vishnoi
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A Mini-Course on Convex Optimization - Notes by Vishnoi
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Introduction to Optimization Theory - Aaron Sidford 2017
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Convex Optimization I - Stephen Boyd & John Duchi
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Convex Optimization II - John Duchi
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Convex Optimization - Pradeep Ravikumar & Aarti Singh 2017
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Convex Optimization and Approximation - Moritz Hardt 2018
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Optimization for Machine Learning - Martin Jaggi
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Convex Optimization - Ryan Tibshirani 2018
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Optimization Methods in Statistics - Ambuj Tewari 2015 (with link to other courses)
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Cheat Sheet - series of posts - Sebastian Pokutta
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Advanced Structured Prediction and Optimization - Simon Lacoste-Julien 2020