Theoretical Computer Science
204 items from mostafatouny/awesome-theoretical-computer-science ★1,220
-
Igor Pak. How to Start a Paper igorpak.wordpress.com
Why should you introduce a conceptual preliminary motivating the story of your paper.
-
Aho & Ullman. Foundations of Computer Science i.stanford.edu
A classic math-oriented introduction to computer science.
-
The Univalent Foundations Program Institute for Advanced Study. Homotopy Type Theory: Univalent Foundations of… homotopytypetheory.org
The present book is intended as a first systematic exposition of the basics of univalent foundations, and a collection of examples of this new style of reasoning — but without requiring the reader to know or learn any formal logic, or to use any computer proof assistant.
-
Lee. Randomized Algorithms and Probabilistic Analysis. Washington. homes.cs.washington.edu
Topics include Discrete probability, High-dimensional geometry and statistics, Information and entropy, and Markov chains and convergence to equilibrium.
-
Madhur Tulsiani. Mathematical Toolkit home.ttic.edu
Things prof. Madhur wish he knew in first year of grad school.
-
Blum. An Introduction to the Theory of Machine Learning. TTIC home.ttic.edu
The basic theory underlying machine learning and the process of generalizing from data.
-
Henry Yuen. The Complexity of Entanglement. Fall 2020 henryyuen.net
Focuses on cutting edge topics in quantum information that relate to Complexity of Entanglement. - see this class also
-
Demaine, Gasarch & Hajiaghayi. Computers and Intractability: A Guide to Algorithmic Lower Bounds hardness.mit.edu
A sequel to Garey and Johnson's Computers and Intractability: A Guide to NP-Completeness. New topics include Parameterized Complexity, Lower bounds on approximation, Other hardness assumptions (ETH, 3SUM-conjecture, APSP-conjecture, UGC, Others), Online Algorithms, Streaming…
-
Xu. Topics in Learning and Game Theory haifeng-xu.com
A graduate level course covering topics at the interface between machine learning and game theory.
-
Yaroslavtsev. Hires spreadsheet 2022 grigory.us
A crowdsourced spreadsheet created to collect information about theory hires in year 2022.
-
Fortnow. The Golden Ticket: P, NP, and the Search for the Impossible goldenticket.fortnow.com
A nontechnical introduction to P-NP, its rich history, and its algorithmic implications for everything we do with computers and beyond.
-
Fang. Advanced Topics in Machine Learning and Game Theory feifang.info
A graduate-level course covering the topics at the intersection of machine learning and game theory.
-
Knuth. The Art of Computer Programming en.wikipedia.org
A legendary series by Donald Knuth on design and analysis of algorithms.
-
EATCS Bulletin eatcs.org
Surveys, tutorials, conferences reports, events, open problems and solutions, PhD Theses, and entertaining contributions.
-
European Association of TCS eatcs.org
-
Theory Announcements dmatheorynet.blogspot.com
DMANET spreads information on conferences, workshops, seminars etc. relating to discrete mathematics and algorithms.
-
ACM's students magazine special issue for theory of computation.
-
SIGACT News dl.acm.org
ACM's official theoretical computer science news feed.
-
Williamson & Shmoys. The Design of Approximation Algorithms designofapproxalgs.com
It includes greedy, local search algorithms, dynamic programming, linear and semidefinite programming, and randomization.
-
Deutsch. The Fabric of Reality: The Science of Parallel Universes and Its Implications daviddeutsch.org.uk
The Fabric of Reality presents a startlingly integrated, rational and optimistic world view – the result of taking seriously the deepest ideas of modern science and the philosophy of science.
-
Salamon's List cstheory.stackexchange.com
Selected Conferences.
-
TCS Stack Exchange cstheory.stackexchange.com
Research-oriented Q&A of theoretical computer science.
-
CS Theory Jobs cstheory-jobs.org
TCS Jobs announcements.
-
CS Theory Events Aggregator cstheory-events.org
An aggregator for CS theory workshops and schools.
-
Agrawal & Jaiswal. When Machine Learning Meets AI and Game Theory cs229.stanford.edu
.
-
Aspnes. Notes on Randomized Algorithms cs.yale.edu
Supplemental notes to the standard books by Mitzenmacher & Upfals, and Motwani & Raghavan.
-
Aspnes. Notes on Discrete Mathematics cs.yale.edu
Fall 2017 of the Yale course CPSC 202a, Mathematical Tools for Computer Science.
-
Watrous. Introduction to The Theory of Computing cs.uwaterloo.ca
undergrad introduction to theory of computation
-
First cs.ubc.ca
and Second Course in Randomized Algorithms. Columbia. - Respectively, undergrad and grad courses for probabilistic methods in algorithms.
-
-
Rubinstein & Weinberg. Research Masters in TCS cs.princeton.edu
A list of master programs in TCS.
-
Livni. COS 511 Theoretical Machine Learning. Princeton cs.princeton.edu
Formally define and study various models that have been proposed for learning. The course will present and contrast the statistical, computational and online models for learning. We will present and rigorously analyze some of the most successful algorithms in machine learning…
-
Arora. Overcoming Intractability in Machine Learning cs.princeton.edu
A seminar course that will focus on the following phenomenon: many problems in machine learning are formally intractable (e.g., NP-hard). Nevertheless they are solved in practice by heuristics. Can we design algorithms with provable guarantees (running time, solution quality)?
-
Arora. The Computational Universe cs.princeton.edu
Takes us on a broad sweep of scientific knowledge and related technologies: propositional logic of the ancient Greeks (microprocessors); quantum mechanics (silicon chips); network and system phenomena (internet and search engines); computational intractability (secure…
-
Arora. Advanced Algorithm Design cs.princeton.edu
Notably uses ideas such as randomness, approximation, high dimensional geometry. Faces uncertainty, approaches to handle big data, handling intractability, heuristic approaches, ..etc.
-
Arora. Thinking Like a Theorist. Princeton cs.princeton.edu
It covers a large number of the math/CS topics that you need to know for reading and doing research in Computer Science Theory.
-
Arora. A Theorist's Toolkit. Princeton cs.princeton.edu
Aimed primarily at first and second year graduate students who plan to do research in theoretical computer science. We will introduce probabilistic, algebraic, combinatorial, and algorithmic methods useful in proofs.
-
Koutsoupias. Probability and Computing. Oxford cs.ox.ac.uk
Introduction to probabilistic methods in computer science.
-
Robert Robere. Proof Complexity: Algorithms and Lower Bounds cs.mcgill.ca
An introduction to modern proof complexity, emphasizing its connections with computational complexity and algorithms in optimization.
-
Dinitz. Approximation Algorithms. Johns Hopkins cs.jhu.edu
It includes greedy, local search, dynamic programming, randomized rounding, tree embeddings, and semidefinite programming.
-
Eva Tardos. Algorithmic Game Theory cs.cornell.edu
It combines algorithmic thinking with game-theoretic, or, more generally, economic concepts. The course will study a range of topics at this interface. The only prerequisite to the course is mathematical thinking.
-
Cornell. Functional Programming in Ocaml cs.cornell.edu
A modern course on data structures and functional programming using OCaml.
-
Halpern. CS 2802: Discrete Structures - Honors. 2020. Cornell cs.cornell.edu
Honors lecture notes on discrete math - Homework
-
O'Donnell. A Theorist's Toolkit. CMU cs.cmu.edu
It covers a large number of the math/CS topics that you need to know for reading and doing research in Computer Science Theory.
-
-
Blum. You and Your Research: An Advice to a Beginning Graduate Student cs.cmu.edu
Manuel Blum, A very popular figure in TCS, gives research advices for juniors.
-
Blum. Intro Machine Learning Theory cs.cmu.edu
.
-
Gupta & Ravi. Approximation Algorithms. CMU cs.cmu.edu
It includes convex programming-based, randomness, and metric methods.
-
Brown. Resources list for game theory cs.brown.edu
TAs based these notes in large part on the lecture notes and accompanying videos of Tim Roughgarden's CS 364A and CS 364B courses at Stanford, and Jason Hartline's Mechanism Design and Approximation textbook.
-
Mark Bun. CS591 Communication Complexity cs-people.bu.edu
A graduate course which introduces the fundamental results and techniques in the area and some research frontier questions. Themes include: Communication models and the communication complexity zoo, Information vs. communication, Query-to-communication lifting, and Applications
- next page of items loading…
