awesome-awesomeness/html/theoreticalcomputerscience.html

<p><img src="./TCS-banner.png" alt="banner" /> # Awesome Theoretical
Computer Science <a href="https://awesome.re"><img
src="https://awesome.re/badge-flat.svg" alt="Awesome" /></a> The
interdisciplinary of Mathematics and Computer Science; It is
distinguished by its emphasis on mathemtical technique and rigour.</p>
<hr />
<h2 id="contents">Contents</h2>
<ul>
<li><a href="#broad_intros">Broad Intros</a>
<ul>
<li><a href="#broad_intros_books">Books</a></li>
</ul></li>
<li><a href="#handbooks">Handbooks</a></li>
<li><a href="#theory_of_computation">Theory of Computation</a>
<ul>
<li><a href="#theory_of_computation_introductory">Introductory</a>
<ul>
<li><a href="#theory_of_computation_introductory_lecture_notes">Lecture
Notes</a> | <a
href="#theory_of_computation_introductory_lecture_videos_playlists">Lecture
Videos Playlists</a> | <a
href="#theory_of_computation_introductory_mooc">MOOC</a> | <a
href="#theory_of_computation_introductory_books">Books</a> | <a
href="#theory_of_computation_introductory_puzzles_and_problem_sets">Puzzles
and Problem Sets</a></li>
</ul></li>
<li><a
href="#theory_of_computation_computational_complexity">Computational
Complexity</a>
<ul>
<li><a
href="#theory_of_computation_computational_complexity_introductory">Introductory</a>
<ul>
<li><a
href="#theory_of_computation_computational_complexity_introductory_lecture_videos_playlists">Lecture
Videos Playlists</a> | <a
href="#theory_of_computation_computational_complexity_introductory_lecture_notes">Lecture
Notes</a> | <a
href="#theory_of_computation_computational_complexity_introductory_books">Books</a></li>
</ul></li>
<li><a
href="#theory_of_computation_computational_complexity_communication_complexity">Communication
Complexity</a>
<ul>
<li><a
href="#theory_of_computation_computational_complexity_communication_complexity_lecture_notes">Lecture
Notes</a> | <a
href="#theory_of_computation_computational_complexity_communication_complexity_books">Books</a></li>
</ul></li>
<li><a
href="#theory_of_computation_computational_complexity_circuit_complexity">Circuit
Complexity</a>
<ul>
<li><a
href="#theory_of_computation_computational_complexity_circuit_complexity_books">Books</a></li>
</ul></li>
<li><a
href="#theory_of_computation_computational_complexity_quantum_complexity">Quantum
Complexity</a>
<ul>
<li><a
href="#theory_of_computation_computational_complexity_quantum_complexity_lecture_videos_playlists">Lecture
Videos Playlists</a> | <a
href="#theory_of_computation_computational_complexity_quantum_complexity_lecture_notes">Lecture
Notes</a></li>
</ul></li>
<li><a
href="#theory_of_computation_computational_complexity_proof_complexity">Proof
Complexity</a>
<ul>
<li><a
href="#theory_of_computation_computational_complexity_proof_complexity_lecture_notes">Lecture
Notes</a></li>
</ul></li>
</ul></li>
<li><a href="#theory_of_computation_computability_theory">Computability
Theory</a>
<ul>
<li><a
href="#theory_of_computation_computability_theory_books">Books</a>
<ul>
<li><a
href="#theory_of_computation_computability_theory_books_introductory">Introductory</a>
| <a
href="#theory_of_computation_computability_theory_books_advanced">Advanced</a>
| <a
href="#theory_of_computation_computability_theory_books_monograph">Monograph</a></li>
</ul></li>
</ul></li>
</ul></li>
<li><a href="#logic">Logic</a>
<ul>
<li><a href="#logic_computational_complexity">Computational
Complexity</a>
<ul>
<li><a href="#logic_computational_complexity_books">Books</a></li>
</ul></li>
</ul></li>
<li><a href="#algorithms">Algorithms</a>
<ul>
<li><a href="#algorithms_general">General</a>
<ul>
<li><a href="#algorithms_general_lecture_notes">Lecture Notes</a> | <a
href="#algorithms_general_books">Books</a></li>
</ul></li>
<li><a href="#algorithms_lower_bounds">Lower Bounds</a>
<ul>
<li><a href="#algorithms_lower_bounds_lecture_videos_playlists">Lecture
Videos Playlists</a> | <a
href="#algorithms_lower_bounds_books">Books</a></li>
</ul></li>
<li><a href="#algorithms_randomization__probability">Randomization &amp;
Probability</a>
<ul>
<li><a
href="#algorithms_randomization__probability_lecture_notes">Lecture
Notes</a></li>
</ul></li>
<li><a href="#algorithms_approximation">Approximation</a>
<ul>
<li><a href="#algorithms_approximation_lecture_notes">Lecture Notes</a>
| <a href="#algorithms_approximation_books">Books</a></li>
</ul></li>
<li><a href="#algorithms_parameterized">Parameterized</a>
<ul>
<li><a href="#algorithms_parameterized_books">Books</a></li>
</ul></li>
</ul></li>
<li><a href="#informationcoding_theory">Information/Coding Theory</a>
<ul>
<li><a href="#informationcoding_theory_lecture_notes">Lecture Notes</a>
| <a href="#informationcoding_theory_workshops">Workshops</a> | <a
href="#informationcoding_theory_conferences">Conferences</a></li>
</ul></li>
<li><a href="#cryptography">Cryptography</a>
<ul>
<li><a href="#cryptography_books">Books</a></li>
</ul></li>
<li><a href="#machine_learning_theory">Machine Learning Theory</a>
<ul>
<li><a href="#machine_learning_theory_lecture_notes">Lecture Notes</a> |
<a href="#machine_learning_theory_books">Books</a> | <a
href="#machine_learning_theory_workshops">Workshops</a> | <a
href="#machine_learning_theory_conferences">Conferences</a> | <a
href="#machine_learning_theory_research_groups">Research Groups</a> | <a
href="#machine_learning_theory_other">Other</a></li>
</ul></li>
<li><a href="#game_theory">Game Theory</a>
<ul>
<li><a href="#game_theory_lecture_notes">Lecture Notes</a> | <a
href="#game_theory_books">Books</a> | <a
href="#game_theory_workshops">Workshops</a></li>
</ul></li>
<li><a href="#physics">Physics</a>
<ul>
<li><a href="#physics_lecture_notes">Lecture Notes</a> | <a
href="#physics_books">Books</a> | <a
href="#physics_monographs">Monographs</a></li>
</ul></li>
<li><a href="#philosophy">Philosophy</a>
<ul>
<li><a href="#philosophy_lecture_notes">Lecture Notes</a> | <a
href="#philosophy_books">Books</a> | <a
href="#philosophy_papers">Papers</a></li>
</ul></li>
<li><a href="#mathlogic_preliminaries">Math/Logic Preliminaries</a>
<ul>
<li><a href="#mathlogic_preliminaries_general">General</a>
<ul>
<li><a
href="#mathlogic_preliminaries_general_lecture_videos_playlist">Lecture
Videos Playlist</a> | <a
href="#mathlogic_preliminaries_general_books">Books</a> | <a
href="#mathlogic_preliminaries_general_lecture_notes">Lecture
Notes</a></li>
</ul></li>
<li><a href="#mathlogic_preliminaries_tcs_inspired">TCS Inspired</a>
<ul>
<li><a
href="#mathlogic_preliminaries_tcs_inspired_lecture_videos_playlists">Lecture
Videos Playlists</a> | <a
href="#mathlogic_preliminaries_tcs_inspired_lecture_notes">Lecture
Notes</a></li>
</ul></li>
<li><a href="#mathlogic_preliminaries_discrete_mathematics">Discrete
Mathematics</a>
<ul>
<li><a
href="#mathlogic_preliminaries_discrete_mathematics_lecture_notes">Lecture
Notes</a> | <a
href="#mathlogic_preliminaries_discrete_mathematics_books">Books</a> |
<a
href="#mathlogic_preliminaries_discrete_mathematics_mooc">MOOC</a></li>
</ul></li>
<li><a
href="#mathlogic_preliminaries_transition_to_pure_rigour_math">Transition
To Pure Rigour Math</a></li>
</ul></li>
<li><a href="#surveys__monographs">Surveys &amp; Monographs</a></li>
<li><a href="#live_content">Live Content</a>
<ul>
<li><a
href="#live_content_conferences_workshops_events_and_talks">Conferences,
Workshops, Events, and Talks</a>
<ul>
<li><a
href="#live_content_conferences_workshops_events_and_talks_aggregators">Aggregators</a>
| <a
href="#live_content_conferences_workshops_events_and_talks_live">Live</a>
| <a
href="#live_content_conferences_workshops_events_and_talks_archived">Archived</a></li>
</ul></li>
<li><a href="#live_content_magazines_news_and_monographs">Magazines,
News, and Monographs</a></li>
<li><a href="#live_content_blogs_aggregators">Blogs Aggregators</a></li>
<li><a href="#live_content_jobs">Jobs</a>
<ul>
<li><a href="#live_content_jobs_aggregator">Aggregator</a> | <a
href="#live_content_jobs_lists">Lists</a></li>
</ul></li>
<li><a href="#live_content_online_communities">Online
Communities</a></li>
</ul></li>
<li><a href="#other_resources">Other Resources</a>
<ul>
<li><a href="#other_resources_blog_posts_and_essays">Blog Posts and
Essays</a> | <a
href="#other_resources_special_magazines_and_workshops">Special
Magazines and Workshops</a> | <a
href="#other_resources_popular_science_books">Popular Science Books</a>
| <a href="#other_resources_cheat_sheets">Cheat Sheets</a> | <a
href="#other_resources_network_groups">Network Groups</a></li>
</ul></li>
<li><a href="#related_awesome_lists">Related Awesome Lists</a></li>
</ul>
<hr />
<h1 id="broad-intros">Broad Intros<a name=broad_intros></a></h1>
<h2 id="books">Books<a name=broad_intros_books></a></h2>
<ul>
<li><a href="http://nature-of-computation.org/">Moore &amp; Mertens. The
Nature of Computation</a> - It spans complexity of mazes and games;
optimization in theory and practice; randomized algorithms, interactive
proofs, and pseudorandomness; Markov chains and phase transitions; and
of quantum computing. It provides accessible explanations</li>
<li><a
href="https://www.math.ias.edu/files/Book-online-Aug0619.pdf">Wigderson.
Mathematics and Computation: A Theory Revolutionizing Technology and
Science</a> - A sweeping survey of complexity theory, emphasizing the
field’s insights and challenges. It explains the ideas and motivations
leading to key models, notions, and results. #
Handbooks<a name=handbooks></a></li>
<li><a
href="https://www.routledge.com/Algorithms-and-Theory-of-Computation-Handbook-Volume-1-General-Concepts/Atallah-Blanton/p/book/9781138113930">Atallah
&amp; Blanton. Algorithms and Theory of Computation Handbook: General
Concepts and Techniques</a> - A complete comprehensive encyclopediac
handbook which surveys all related areas to theoretical computer
science.</li>
<li><a
href="https://www.routledge.com/Algorithms-and-Theory-of-Computation-Handbook-Volume-2-Special-Topics/Atallah-Blanton/p/book/9780367384845">Atallah
&amp; Blanton. Algorithms and Theory of Computation Handbook: Special
Topics and Techniques</a> - A complete comprehensive encyclopediac
handbook which surveys all related areas to theoretical computer
science.</li>
<li><a
href="https://mitpress.mit.edu/books/handbook-theoretical-computer-science-volume">Handbook
of Theoretical Computer Science. Volume A: Algorithms and Complexity</a>
- A complete comprehensive encyclopediac handbook which surveys all
related areas to theoretical computer science.</li>
<li><a
href="https://mitpress.mit.edu/books/handbook-theoretical-computer-science-2-vol-set">Handbook
of Theoretical Computer Science. Volume B: Formal Methods and
Semantics</a> - A complete comprehensive encyclopediac handbook which
surveys all related areas to theoretical computer science. # Theory of
Computation<a name=theory_of_computation></a> ##
Introductory<a name=theory_of_computation_introductory></a> ### Lecture
Notes<a name=theory_of_computation_introductory_lecture_notes></a></li>
<li><a href="https://cs.uwaterloo.ca/~watrous/ToC-notes/">Watrous.
Introduction to The Theory of Computing</a> - undergrad introduction to
theory of computation ### Lecture Videos
Playlists<a name=theory_of_computation_introductory_lecture_videos_playlists></a></li>
<li><a
href="https://www.youtube.com/playlist?list=PLCqUsBXxq16yBaN_hpo7dY2l9N-ZLtI-X">Yanofsky.
Theoretical Computer Science</a> - undergrad introduction to theory of
computation</li>
<li><a
href="https://www.youtube.com/playlist?list=PLKzLTB8HeSUIuln-o1mbXfTr8HmIhiGEg">Anil
Ada. Great Ideas in Theoretical Computer Science. CMU</a> - A series of
lectures on selected notable topics in theoretical computer
science.</li>
<li><a
href="https://www.youtube.com/playlist?list=PLm3J0oaFux3aafQm568blS9blxtA_EWQv">O’Donnell.
Great Ideas in Theoretical Computer Science. CMU</a> - A series of
lectures on selected notable topics in theoretical computer science. ###
MOOC<a name=theory_of_computation_introductory_mooc></a></li>
<li><a
href="https://www.udacity.com/course/intro-to-theoretical-computer-science--cs313">Intro
to Theoretical Computer Science</a> - It teaches basic concepts in
theoretical computer science, such as NP-completeness, and what they
imply for solving tough algorithmic problems.</li>
<li><a
href="https://www.udacity.com/course/computability-complexity-algorithms--ud061">Computability,
Complexity &amp; Algorithms. Georgia Institute of Technology</a> - It
focuses on the big fundamental questions of computing, and how
understanding the power and limitations of algorithms helps us develop
the tools to make real-world computers smarter, faster and safer. ###
Books<a name=theory_of_computation_introductory_books></a></li>
<li><a
href="https://www.cengage.com/c/introduction-to-the-theory-of-computation-3e-sipser/9781133187790/">Sipser.
Introduction to Theory of Computation</a> - A standard text for
introducing theory of computation for undergrads.</li>
<li><a
href="https://www.pearson.com/us/higher-education/program/Hopcroft-Introduction-to-Automata-Theory-Languages-and-Computation-3rd-Edition/PGM64331.html">Hopcroft,
Motwani &amp; Ullman. Introduction to Automata Theory, Languages, and
Computation</a> - Introductory undergrad textbook for automata,
languages and theory of computation topics.</li>
<li><a href="https://introtcs.org/public/index.html">Barak. Introduction
to TCS</a> - A modern, brief, and accessible text which introduces
theoretical computer science for undergrads. It includes topics not
usually included in standard undergrad text-books. ### Puzzles and
Problem
Sets<a name=theory_of_computation_introductory_puzzles_and_problem_sets></a></li>
<li><a
href="https://onlinelibrary.wiley.com/doi/book/10.1002/0471224642">Zhu
&amp; Ko. Problem Solving in Automata, Languages, and Complexity</a> - A
problem-set text for automata, languages, and complexity. ##
Computational
Complexity<a name=theory_of_computation_computational_complexity></a>
###
Introductory<a name=theory_of_computation_computational_complexity_introductory></a>
#### Lecture Videos
Playlists<a name=theory_of_computation_computational_complexity_introductory_lecture_videos_playlists></a></li>
<li><a
href="https://www.youtube.com/playlist?list=PLm3J0oaFux3YL5vLXpzOyJiLtqLp6dCW2">O’Donnell.
Undergrad Complexity Theory. Fall 2019 (15-455)</a> (<a
href="https://www.cs.cmu.edu/~odonnell/15455-s17/">Homework</a>) -
Undergraduate course on computational complexity theory; It follows the
same spirit of Sipser’s part III.</li>
<li><a
href="https://www.youtube.com/playlist?list=PLm3J0oaFux3b8Gg1DdaJOzYNsaXYLAOKH">O’Donnell.
Graduate Complexity Theory</a> - It covers most of what is believed to
be known to get started in complexity theory research. #### Lecture
Notes<a name=theory_of_computation_computational_complexity_introductory_lecture_notes></a></li>
<li><a href="http://www.ams.org/books/pcms/010/">Rudich &amp; Wigderson.
Computational Complexity Theory</a> - Three weeks of lectures from the
IAS/Park City Mathematics Institute Summer School on computational
complexity. Topics include reductions, lower-bounds, average-case
complexity, randomness, interactive proof systems, probabilistically
checkable proofs, quantum computing, and proof complexity. ####
Books<a name=theory_of_computation_computational_complexity_introductory_books></a></li>
<li><a href="https://theory.cs.princeton.edu/complexity/book.pdf">Arora
&amp; Barak. Computational Complexity: A Modern Approach</a> - A golden
standard textbook, Surveying computational complexity theory for
graduate students and researchers.</li>
<li><a
href="http://www.wisdom.weizmann.ac.il/~oded/cc-book.html">Goldreich.
Computational Complexity: A Conceptual Perspective</a> - A grad
introduction to computation complexity theory, emphasizing the idea
behind concepts of complexity theory.</li>
<li><a
href="http://www.wisdom.weizmann.ac.il/~oded/bc-book.html">Goldreich. P,
NP, and NP-Completeness: The Basics of Computational Complexity</a> - A
very gentle introduction to some fundamental ideas of computational
complexity like NP-completeness and P vs NP.</li>
<li><a href="https://www.springer.com/gp/book/9783540674191">Ogihara
&amp; Hemaspaandra. The Complexity Theory Companion</a> - An accessible,
algorithmically oriented, research-centered, up-to-date guide to some of
the most interesting techniques of complexity theory.</li>
<li><a
href="https://www.pearson.com/us/higher-education/program/Papadimitriou-Computational-Complexity/PGM94583.html">Papadimitriou.
Computational Complexity</a> - Body of knowledge for studying the
performance and limitations of computer algorithms. Among topics covered
are: reductions and NP-completeness, cryptography and protocols,
randomized algorithms, and approximability of optimization problems,
circuit complexity, the structural aspects of the P=NP question,
parallel computation, and the polynomial hierarchy. ### Communication
Complexity<a name=theory_of_computation_computational_complexity_communication_complexity></a>
#### Lecture
Notes<a name=theory_of_computation_computational_complexity_communication_complexity_lecture_notes></a></li>
<li><a href="https://cs-people.bu.edu/mbun/courses/591_F19/">Mark Bun.
CS591 Communication Complexity</a> - 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 ####
Books<a name=theory_of_computation_computational_complexity_communication_complexity_books></a></li>
<li><a
href="https://www.cambridge.org/core/books/communication-complexity/5F44993E3B2597174B71D3F21E748443">Rao
&amp; Yehudayoff. Communication Complexity and Applications</a> - An
excellent and very readable introductory textbook to the field of
communication complexity. ### Circuit
Complexity<a name=theory_of_computation_computational_complexity_circuit_complexity></a>
####
Books<a name=theory_of_computation_computational_complexity_circuit_complexity_books></a></li>
<li><a href="https://www.springer.com/gp/book/9783642245077">Jukna.
Boolean Function Complexity: Advances and Frontiers</a> - A modern
textbook surveying circuit complexity.</li>
<li><a href="https://www.springer.com/gp/book/9783540594369">Clote &amp;
Kranakis. Boolean Functions and Computation Models</a> - An introduction
to circuit complexity, boolean functions, and computation models. ###
Quantum
Complexity<a name=theory_of_computation_computational_complexity_quantum_complexity></a>
#### Lecture Videos
Playlists<a name=theory_of_computation_computational_complexity_quantum_complexity_lecture_videos_playlists></a></li>
<li><a
href="https://www.youtube.com/playlist?list=PLZGjbQcY0aI7Yqwbwp-lsf1tTPyvkQG6h">Uni
Paderborn. Quantum Complexity Theory. Winter 2020</a> - CS Masters level
lectures on topics including Boson sampling, quantum interactive proofs,
and quantum merlin arthur. #### Lecture
Notes<a name=theory_of_computation_computational_complexity_quantum_complexity_lecture_notes></a></li>
<li><a
href="https://www.henryyuen.net/fall2020/complexity_of_entanglement_notes.pdf">Henry
Yuen. The Complexity of Entanglement. Fall 2020</a> - Focuses on cutting
edge topics in quantum information that relate to Complexity of
Entanglement. - see this <a
href="https://www.henryyuen.net/classes/fall2020/">class</a> also ###
Proof
Complexity<a name=theory_of_computation_computational_complexity_proof_complexity></a>
#### Lecture
Notes<a name=theory_of_computation_computational_complexity_proof_complexity_lecture_notes></a></li>
<li><a href="https://www.cs.mcgill.ca/~robere/comp598/index.html">Robert
Robere. Proof Complexity: Algorithms and Lower Bounds</a> - An
introduction to modern proof complexity, emphasizing its connections
with computational complexity and algorithms in optimization. ##
Computability
Theory<a name=theory_of_computation_computability_theory></a> ###
Books<a name=theory_of_computation_computability_theory_books></a> ####
Introductory<a name=theory_of_computation_computability_theory_books_introductory></a></li>
<li><a
href="https://www.cambridge.org/highereducation/books/computability/E8F085FDBECB8280F7723D71C1D2EE1C">Cutland.
Computability: An Introduction to Recursive Function Theory</a> -
Intuitively, It explains the idea of a computable function: a function
whose values can be calculated in an effective or automatic way.</li>
<li><a
href="https://www.routledge.com/Computability-Theory/Cooper-Cooper/p/book/9781584882374">Cooper.
Computability Theory</a> - A concise, comprehensive, and authoritative
introduction to contemporary computability theory, techniques, and
results.</li>
<li><a
href="https://www.amazon.com/Computability-Unsolvability-Prof-Martin-Davis/dp/0486614719">Davis.
Computability and Unsolvability</a> - In this classic text, Dr. Davis
provides a clear introduction to computability, at an advanced
undergraduate level, that serves the needs of specialists and
non-specialists alike. ####
Advanced<a name=theory_of_computation_computability_theory_books_advanced></a></li>
<li><a href="https://www.springer.com/gp/book/9783540666813">Soare.
Recursively Enumerable Sets and Degree</a> - It gives a complete account
of the theory of r.e degrees. The definitions, results and proofs are
always clearly motivated and explained before the formal presentation;
the proofs are described with remarkable clarity and conciseness.</li>
<li><a
href="https://archive.org/details/classicalrecursi0000odif">Odifreddi.
Classical Recursion Theory: The Theory of Functions and Sets of Natural
Numbers</a> - An impressive presentation of classical recursion theory.
It is highly recommended to everyone interested in recursion theory.
####
Monograph<a name=theory_of_computation_computability_theory_books_monograph></a></li>
<li><a href="https://mitpress.mit.edu/books/computability">Copeland,
Posy &amp; Shagrir (editors). Computability: Turing, Gödel, Church, and
Beyond</a> - Computer scientists, mathematicians, and philosophers
discuss the conceptual foundations of the notion of computability as
well as recent theoretical developments. # Logic<a name=logic></a> ##
Computational Complexity<a name=logic_computational_complexity></a> ###
Books<a name=logic_computational_complexity_books></a></li>
<li><a href="https://www.springer.com/gp/book/9783319001180">Pudlák.
Logical Foundations of Mathematics and Computational Complexity: A
Gentle Introduction</a> - Presents a wide range of results in logic and
computational complexity. # Algorithms<a name=algorithms></a> ##
General<a name=algorithms_general></a> ### Lecture
Notes<a name=algorithms_general_lecture_notes></a></li>
<li><a
href="https://www.cs.princeton.edu/courses/archive/fall15/cos521/">Arora.
Advanced Algorithm Design</a> - Notably uses ideas such as randomness,
approximation, high dimensional geometry. Faces uncertainty, approaches
to handle big data, handling intractability, heuristic approaches,
..etc. ### Books<a name=algorithms_general_books></a></li>
<li><a
href="https://en.wikipedia.org/wiki/The_Art_of_Computer_Programming">Knuth.
The Art of Computer Programming</a> - A legendary series by Donald Knuth
on design and analysis of algorithms. ## Lower
Bounds<a name=algorithms_lower_bounds></a> ### Lecture Videos
Playlists<a name=algorithms_lower_bounds_lecture_videos_playlists></a></li>
<li><a
href="https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-890-algorithmic-lower-bounds-fun-with-hardness-proofs-fall-2014/">Demaine.
Algorithmic Lower Bounds: Fun with Hardness Proofs</a> - A class taking
a practical approach to proving problems can’t be solved efficient. ###
Books<a name=algorithms_lower_bounds_books></a></li>
<li><a href="https://hardness.mit.edu/">Demaine, Gasarch &amp;
Hajiaghayi. Computers and Intractability: A Guide to Algorithmic Lower
Bounds</a> - 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 Algorithms, Polynomial Parity Arguments, and
Parallelism.</li>
<li><a
href="https://www.routledge.com/Games-Puzzles-and-Computation/Hearn-Demaine/p/book/9781568813226">Demaine.
Games, Puzzles, and Computation</a> - It shows that games and puzzles
can serve as powerful models of computation, Offering a new way of
thinking about computation. ## Randomization &amp;
Probability<a name=algorithms_randomization__probability></a> ###
Lecture
Notes<a name=algorithms_randomization__probability_lecture_notes></a></li>
<li><a
href="https://web.stanford.edu/class/archive/cs/cs265/cs265.1232/">Mary
Wootters. Randomized Algorithms and Probabilistic Analysis. Stanford</a>
- Key tools of probabilistic analysis, and application of these tools to
understand the behaviors of random processes and algorithms. Emphasis is
on theoretical foundations, though applications will be discussed in
machine learning and data analysis, networking, and systems. Topics
include tail bounds, the probabilistic method, Markov chains, and
martingales, with applications to analyzing random graphs, metric
embeddings, and random walks.</li>
<li><a
href="https://www.cs.ox.ac.uk/people/elias.koutsoupias/pc2018-19/">Koutsoupias.
Probability and Computing. Oxford</a> - Introduction to probabilistic
methods in computer science.</li>
<li>Harvey. <a href="https://www.cs.ubc.ca/~nickhar/Book1.pdf">First</a>
and <a href="https://www.cs.ubc.ca/~nickhar/Book2.pdf">Second</a> Course
in Randomized Algorithms. Columbia. - Respectively, undergrad and grad
courses for probabilistic methods in algorithms.</li>
<li><a
href="https://homes.cs.washington.edu/~jrl/teaching/cse525sp19/">Lee.
Randomized Algorithms and Probabilistic Analysis. Washington.</a> -
Topics include Discrete probability, High-dimensional geometry and
statistics, Information and entropy, and Markov chains and convergence
to equilibrium.</li>
<li><a
href="https://www.cs.yale.edu/homes/aspnes/classes/469/notes.pdf">Aspnes.
Notes on Randomized Algorithms</a> - Supplemental notes to the standard
books by Mitzenmacher &amp; Upfals, and Motwani &amp; Raghavan. ##
Approximation<a name=algorithms_approximation></a> ### Lecture
Notes<a name=algorithms_approximation_lecture_notes></a></li>
<li><a href="https://courses.engr.illinois.edu/cs583/fa2021/">Chekuri.
Approximation Algorithmis Illinois</a> - A broad introduction to results
and techniques with an emphasis on fundamental problems and widely
applicable tools. Also more advanced and specialized topics.</li>
<li><a
href="https://www.cs.jhu.edu/~mdinitz/classes/ApproxAlgorithms/Spring2021/">Dinitz.
Approximation Algorithms. Johns Hopkins</a> - It includes greedy, local
search, dynamic programming, randomized rounding, tree embeddings, and
semidefinite programming.</li>
<li><a
href="http://www.cs.cmu.edu/afs/cs/academic/class/15854-f05/www/">Gupta
&amp; Ravi. Approximation Algorithms. CMU</a> - It includes convex
programming-based, randomness, and metric methods. ###
Books<a name=algorithms_approximation_books></a></li>
<li><a href="https://www.designofapproxalgs.com/">Williamson &amp;
Shmoys. The Design of Approximation Algorithms</a> - It includes greedy,
local search algorithms, dynamic programming, linear and semidefinite
programming, and randomization.</li>
<li><a
href="https://u.pcloud.link/publink/show?code=XZpzNWXZSCkVs6BKd5RzyNhoRzfJCJoaqSok">Du
&amp; Ko. Design and Analysis of Approximation Algorithms</a> - A
technique-oriented approach provides a unified view. It includes
detailed algorithms, proofs, analyses, examples, and applications from
research papers.</li>
<li><a
href="https://u.pcloud.link/publink/show?code=XZgHNWXZkdvT8L18drSSgLP9vqBIDmbPreD7">Vijay
Vazirani. Approximation Algorithms</a> ##
Parameterized<a name=algorithms_parameterized></a> ###
Books<a name=algorithms_parameterized_books></a></li>
<li>Fedor Fomin. Parametrized Algorithms - Modern comprehensive
explanation of recent tools and techniques with exercises, for graduate
students. # Information/Coding
Theory<a name=informationcoding_theory></a> ## Lecture
Notes<a name=informationcoding_theory_lecture_notes></a></li>
<li><a
href="http://people.seas.harvard.edu/~madhusudan/courses/Spring2020/">Madhu
Sudan. Essential Coding Theory</a> - Some elements of Algorithmic tasks
of encoding and decoding and its connections with error-correction;
These codes are now tools in the design and analysis of algorithms, and
also in many aspects of computational complexity. The focus is on
constructions of algorithmic and asymptotic importance. Requires only
basic mathematical maturity.</li>
<li>Scott Aaronson. Quantum Information Science. <a
href="https://www.scottaaronson.com/qclec.pdf">Part I</a> &amp; <a
href="https://www.scottaaronson.com/qisii.pdf">Part II</a> - Part I:
Presuppose only linear algebra and a bit of classical algorithms. Topics
include quantum circuits, density matrices, entanglement entropy,
Wiesner’s quantum money, QKD, quantum teleportation, the Bell
inequality, interpretations of QM, the Shor 9-qubit code, and the
algorithms of Deutsch-Jozsa, Bernstein-Vazirani, Simon, Shor, and
Grover. Part II: Perspectives on quantum computing that go beyond the
bare quantum circuit model, like Hamiltonians, Stabilizer formalism,
Bosons and Fermions, Cluster states, and Matrix product states. ##
Workshops<a name=informationcoding_theory_workshops></a></li>
<li><a href="https://simons.berkeley.edu/programs/inftheory2015">Simons
Institute. Information Theory Program</a> - It aims to strengthen the
ties between computation and communication communities. It explores (1)
information theoretic techniques in complexity theory and combinatorics,
(2) Coding theory and applications, and (3) information theory, machine
learning, and big data. ##
Conferences<a name=informationcoding_theory_conferences></a></li>
<li><a
href="https://sites.google.com/view/compression-computation-2022/program">Compression+Computation
2022</a> - It bridges the gap of Theoretical Computer Science and
Bioinformatics communities, On new data compression techniques, and
computation over compressed data. #
Cryptography<a name=cryptography></a> ##
Books<a name=cryptography_books></a></li>
<li><a
href="https://link.springer.com/book/10.1007/978-3-319-57048-8">Lindell.
Tutorials on the Foundations of Cryptography</a> - Advanced tutorials
appropriate for self-study by experienced researchers,</li>
<li><a
href="https://www.wisdom.weizmann.ac.il/~oded/book1.html">Goldreich.
Modern Cryptography, Probabilistic Proofs and Pseudorandomness</a> - An
introduction to the interwoven domains of cryptography, proofs and
randomness.</li>
<li><a href="http://www.wisdom.weizmann.ac.il/~oded/rnd.html">Goldreich.
Randomized Methods in Computation</a> - The aim of the current course is
to make the students familiar with some of randomized methods. # Machine
Learning Theory<a name=machine_learning_theory></a> ## Lecture
Notes<a name=machine_learning_theory_lecture_notes></a></li>
<li><a href="https://home.ttic.edu/~avrim/MLT20/">Blum. An Introduction
to the Theory of Machine Learning. TTIC</a> - The basic theory
underlying machine learning and the process of generalizing from
data.</li>
<li><a href="https://mjt.cs.illinois.edu/dlt/">Telgarsky. Deep Learning
Theory. Illinois</a> - Focuses on simplified proofs over what appears in
the literature, and classical perspective of achieving a low test error
for binary classification with IID data via standard (typically ReLU)
feedforward networks.</li>
<li><a href="http://www.jennwv.com/courses/F11.html">Vaughan. CS260:
Machine Learning Theory</a> - A broad overview of the theoretical
foundations underlying common machine learning algorithms.</li>
<li><a
href="https://www.cs.princeton.edu/~rlivni/cos511/cos511.html">Livni.
COS 511 Theoretical Machine Learning. Princeton</a> - 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 that are
extensively used today.</li>
<li><a href="https://people.csail.mit.edu/moitra/408b.html">Moitra.
Theoretical Foundations for Deep Learning. MIT</a> - It explores
theoretical foundations for deep learning, emphasizing the following
themes: (1) Approximation: What sorts of functions can be represented by
deep networks, and does depth provably increase the expressive power?
(2) Optimization: Essentially all optimization problems we want to solve
in practice are non-convex. What frameworks can be used to analyze such
problems? (3) Beyond-Worst Case Analysis: Deep networks can memorize
worst-case data, so why do they generalize well on real-world data?</li>
<li><a
href="https://www.cs.princeton.edu/courses/archive/spring15/cos598D/">Arora.
Overcoming Intractability in Machine Learning</a> - 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)? ##
Books<a name=machine_learning_theory_books></a></li>
<li><a
href="https://mitpress.mit.edu/books/introduction-computational-learning-theory">Vazirani
&amp; Kearns. An Introduction to Computational Learning Theory</a> -
Emphasizing issues of computational efficiency, It introduces a number
of central topics in computational learning theory.</li>
<li><a
href="https://www.cambridge.org/core/books/understanding-machine-learning/3059695661405D25673058E43C8BE2A6">Shalev-Shwartz.
Understanding Machine Learning: From Theory to Algorithms</a> - It
provides an extensive theoretical account of the fundamental ideas
underlying machine learning and the mathematical derivations that
transform these principles into practical algorithms. ##
Workshops<a name=machine_learning_theory_workshops></a></li>
<li><a href="https://simons.berkeley.edu/programs/dl2019">Simons
Institute. Foundations of Deep Learning Program</a> - Aligning and
focusing theoretical and applied researchers on the common purpose of
building empirically relevant theoretical foundations of deep learning.
Specifically, the intention was to identify and make progress on
challenges that, on one hand, are key to guiding the real-world use of
deep learning and, on the other hand, can be approached using
theoretical methodology.</li>
<li><a
href="https://simons.berkeley.edu/programs/datascience2018">Simons
Institute. Foundations of Data Science</a> - Identifying a set of core
techniques and principles that form a foundation for the subject.</li>
<li><a
href="https://simons.berkeley.edu/programs/machinelearning2017">Foundations
of Machine Learning</a> - Aims to grow the reach and impact of computer
science theory within machine learning.</li>
<li><a
href="https://unsupervised.cs.princeton.edu/deeplearningtutorial.html">Toward
Theoretical Understanding of Deep Learning</a></li>
<li><a href="https://simons.berkeley.edu/talks/tbd-288">A Brief
Introduction to Theoretical Foundations of Machine Learning and Machine
Teaching</a> - Formal methods and machine learning can inform each other
from deductive and inductive reasoning perspectives. This talk aims to
facilitate the dialogue between the two communities by establishing some
fundamental concepts in learning theory. ##
Conferences<a name=machine_learning_theory_conferences></a></li>
<li><a href="https://www.learningtheory.org/">COLT</a>. ## Research
Groups<a name=machine_learning_theory_research_groups></a></li>
<li><a
href="https://simons.berkeley.edu/research-pods/machine-learning-research-pod">Simons
Institute. Machine Learning Research Pod</a>.</li>
<li><a href="https://mltheory.org/">Harvard Machine Learning Foundations
Group</a>. ## Other<a name=machine_learning_theory_other></a></li>
<li><a href="https://www.cs.cmu.edu/~avrim/Talks/mlt.pdf">Blum. Intro
Machine Learning Theory</a>.</li>
<li><a href="https://www.cs.cmu.edu/~mblum/search/AGTML35.pdf">Blum,
et.al. Machine Learning, Game Theory, and Mechanism Design for a
Networked World</a>.</li>
<li><a
href="https://cs229.stanford.edu/proj2012/AgrawalJaiswal-WhenMachineLearningMeetsAIandGameTheory.pdf">Agrawal
&amp; Jaiswal. When Machine Learning Meets AI and Game Theory</a>. #
Game Theory<a name=game_theory></a> ## Lecture
Notes<a name=game_theory_lecture_notes></a></li>
<li><a href="https://arxiv.org/abs/1801.00734">Tim Roughgarden.
Complexity Theory, Game Theory, and Economics: The Barbados Lectures</a>
- A mini-course notes of two-fold goals: mini-course is twofold: (i)
Explain how complexity theory has helped illuminate several barriers in
economics and game theory; and (ii) Illustrate how game-theoretic
questions have led to new and interesting complexity theory, including
recent several breakthroughs.</li>
<li><a href="http://www.cs.cornell.edu/courses/cs6840/2012sp/">Eva
Tardos. Algorithmic Game Theory</a> - 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.</li>
<li><a
href="https://chekuri.cs.illinois.edu/teaching/spring2008/agt.htm">Chekuri.
Topics in Algorithms: Algorithmic Game Theory</a> - A broad
graduate-level introduction to: auctions, existence and computation of
equilibria in games and markets, algorithmic mechanism design, price of
anarchy and price of stability, games relevant to networks and
e-commerce. The emphasis will be on conceptual ideas and algorithmic
aspects. No familiarity with game theory or economics will be
assumed.</li>
<li><a href="https://ml2.inf.ethz.ch/courses/agt/">Penna. Algorithmic
Game Theory</a> - The course discusses algorithmic aspects of game
theory, such as a general introduction to game theory, auctions,
mechanisms, the costs of a central control optimum versus those of an
equilibrium under selfish agents, and algorithms and complexity of
computing equilibria.</li>
<li><a href="http://cs.brown.edu/courses/cs1951k/lectures/">Brown.
Resources list for game theory</a> - 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.</li>
<li><a
href="https://feifang.info/advanced-topics-in-machine-learning-and-game-theory-fall-2021/">Fang.
Advanced Topics in Machine Learning and Game Theory</a> - A
graduate-level course covering the topics at the intersection of machine
learning and game theory.</li>
<li><a href="http://www.haifeng-xu.com/cs6501sp21/index.htm">Xu. Topics
in Learning and Game Theory</a> - A graduate level course covering
topics at the interface between machine learning and game theory.</li>
<li><a href="https://timroughgarden.github.io/fob21/">Tim Roughgarden.
Foundations of Blockchains</a> - The science and technology of
blockchain protocols and the applications built on top of them, with an
emphasis on fundamental principles rather than specific protocols. - See
also <a
href="https://www.youtube.com/playlist?list=PLEGCF-WLh2RLOHv_xUGLqRts_9JxrckiA">Lecture
Videos</a>. ## Books<a name=game_theory_books></a></li>
<li><a
href="https://www.cambridge.org/us/academic/subjects/computer-science/programming-languages-and-applied-logic/lectures-game-theory-computer-scientists">Apt
&amp; Grädel. Lectures in Game Theory for Computer Scientists</a> -
Games provide mathematical models for interaction, and numerous tasks in
computer science can be formulated in game-theoretic terms.</li>
<li><a
href="https://www.cambridge.org/core/books/algorithmic-game-theory/0092C07CA8B724E1B1BE2238DDD66B38#fndtn-information">Eva
Tardos &amp; et.al. Algorithmic Game Theory</a> - Basic chapters on
algorithmic methods for equilibria, mechanism design and combinatorial
auctions are followed by chapters on important game theory applications
such as incentives and pricing, cost sharing, information markets and
cryptography and security. ##
Workshops<a name=game_theory_workshops></a></li>
<li><a href="https://simons.berkeley.edu/programs/economics2015">Simons
Institute. Economics and Computation Program</a> - The intersection is
motivated by applications such as large-scale digital auctions and
markets, and fundamental questions such as the computational complexity
of Nash equilibria and complexity and approximation in mechanism design.
Also, To productively model and study the Internet and its novel
computational phenomena, Models and insights can be gained from from
game theory and economic theory. The computational point of view, on the
other hand, is essential to understand a world in which markets are
networked and the default platforms of economic transactions are
algorithmic.</li>
<li><a href="https://simons.berkeley.edu/programs/games2022">Simons
Institute. Learning and Games Program</a> - The intersection is
manifested by (1) Data input to machine learning algorithms are
generated by self-interested parties, (2) Machine learning is used to
optimize economic systems or acts, (3) Machine learning models used in
critical systems are becoming prone to adversarial attacks, and (4)
Several machine learning approaches can be framed as finding the
equilibrium of a game.</li>
<li><a
href="https://simons.berkeley.edu/events/openlectures2015-fall-1">Eva
Tardos. Learning and Efficiency in Games</a> - How to quantify the
impact of strategic user behavior on overall performance in games
including traffic routing as well as online auctions. #
Physics<a name=physics></a> ## Lecture
Notes<a name=physics_lecture_notes></a></li>
<li><a
href="https://www.cs.princeton.edu/courses/archive/spring11/cos116/lectures.php">Arora.
The Computational Universe</a> - 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 encryption); and efficient algorithms (genomic
sequencing). ## Books<a name=physics_books></a></li>
<li><a
href="https://www.taylorfrancis.com/books/feynman-computation-anthony-hey/e/10.1201/9780429500459">Feynman.
Feynman And Computation: Exploring The Limits Of Computers</a></li>
<li>Feynman’s Course on Computation - See also Preskill’s update 40
years later <a href="https://arxiv.org/abs/2106.10522">here</a> ##
Monographs<a name=physics_monographs></a></li>
<li><a
href="https://link.springer.com/book/10.1007/978-3-030-45109-7">Susskind.
Three Lectures on Complexity and Black Holes</a> - Important connections
between thermodynamics and complexity are proposed and discussed.
Pedagogically written, serves as a fundamental introduction to black
holes and their complex physical interpretation #
Philosophy<a name=philosophy></a> ## Lecture
Notes<a name=philosophy_lecture_notes></a></li>
<li><a
href="https://stellar.mit.edu/S/course/6/fa11/6.893/index.html">6.893
Philosophy and Theoretical Computer Science. MIT</a> - It examines the
relevance of modern theoretical computer science to traditional
questions in philosophy, and conversely, what philosophy can contribute
to theoretical computer science. ##
Books<a name=philosophy_books></a></li>
<li><a
href="https://web.stanford.edu/group/cslipublications/cslipublications/site/1575863278.shtml">Knuth.
Things a Computer Scientist Rarely Talks About</a> - A general
illustration of relations between faith and science.</li>
<li><a href="https://www.springer.com/gp/book/9783319532783">Floyd &amp;
Bokulich. Philosophical Explorations of the Legacy of Alan Turing:
Turing 100</a> - Turing’s place in the history and philosophy of
science. ## Papers<a name=philosophy_papers></a></li>
<li><a href="https://www.scottaaronson.com/papers/philos.pdf">Aaronson.
Why Should Philosophers Care About Computational Complexity Theory</a> -
It argues that computational complexity theory leads to new perspectives
on the nature of mathematical knowledge and other philosophical
questions.</li>
<li><a
href="https://www.researchgate.net/publication/227171743_Is_Quantum_Mechanics_Falsifiable_A_computational_perspective_on_thefoundations_of_Quantum_Mechanics">Aharonov
&amp; Vazirani, Is Quantum Mechanics Falsifiable? A Computational
Perspective on the Foundations of Quantum Mechanics</a> - It describes
how quantum mechanics can be tested in the limit of high complexity
regime by extending the usual scientific paradigm to include.</li>
<li><a
href="https://academic.oup.com/philmat/article/27/3/381/5613215">Walter
Dean. Computational Complexity Theory and the Philosophy of
Mathematics</a> - It highlights the significance of complexity theory
relative to questions traditionally asked by philosophers of mathematics
while also attempting to isolate some new ones.</li>
<li><a
href="https://plato.stanford.edu/entries/computational-complexity/">Stanford
Encyclopedia of Philosophy. Computational Complexity Theory</a> - The
foundations of complexity theory, and its potential significance on
philosophy of computer science, philosophy of mathematics and
epistemology.</li>
<li><a href="https://www.jstor.org/stable/40247755">Philip Davis. Toward
a Philosophy of Computation</a> - Philosophical implication of
mathematization and computerization of the world. # Math/Logic
Preliminaries<a name=mathlogic_preliminaries></a> ##
General<a name=mathlogic_preliminaries_general></a> ### Lecture Videos
Playlist<a name=mathlogic_preliminaries_general_lecture_videos_playlist></a></li>
<li><a
href="https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-spring-2015/lecture-slides/">Lehman,
Leighton &amp; Meyer. Mathematics for Computer Science</a> - An
introduction to discrete mathematics oriented toward computer science
and engineering. - <a
href="https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-spring-2015/readings/MIT6_042JS15_textbook.pdf">Companion
Textbook</a> ###
Books<a name=mathlogic_preliminaries_general_books></a></li>
<li><a
href="https://www.pearson.com/us/higher-education/product/Graham-Concrete-Mathematics-A-Foundation-for-Computer-Science-2nd-Edition/9780134389981.html">Knuth,
Graham &amp; Patashnik. Concrete Mathematics: A Foundation for Computer
Science</a> - An expansion of the Mathematical Preliminaries section in
Knuth’s classic Art of Computer Programming, but the style of
presentation is more leisurely, and individual topics are covered more
deeply.</li>
<li><a href="http://i.stanford.edu/~ullman/focs.html">Aho &amp; Ullman.
Foundations of Computer Science</a> - A classic math-oriented
introduction to computer science.</li>
<li><a
href="https://textbooks.open.tudelft.nl/textbooks/catalog/book/13">Tu
Delft. Delftse Foundations of Computation</a> - A textbook for a one
quarter introductory course in theoretical computer science.</li>
<li><a href="https://www.springer.com/series/5517">Comprehensive
Mathematics for Computer Scientists</a> - A series dedicated to math
topics and their relevance to computer science.</li>
<li><a
href="https://www.maa.org/press/maa-reviews/handbook-of-logic-and-proof-techniques-for-computer-science">Krantz.
Handbook of Logic and Proof Techniques for Computer Science</a> - A
concise offered as an accessible reference on mathematical logic for the
professional computer scientist.</li>
<li><a href="https://www.springer.com/gp/book/9783030422172">Makinson.
Sets, Logic and Maths for Computing</a> - It presents a careful
selection of the material most needed by students in their first two
years studying computer science.</li>
<li><a href="https://www.springer.com/gp/book/9781493932221">Yves
Nievergelt. Logic, Mathematics, and Computer Science: Modern Foundations
with Practical Applications</a> - For lower undergraduates, It
introduces the reader to logic, proofs, sets, and number theory,
Focusing on foundations. It provides complete details and derivations of
formal proofs.</li>
<li><a
href="https://link.springer.com/book/10.1007/978-3-030-64811-4">Lacona.
LOGIC: Lecture Notes for Philosophy, Mathematics, and Computer
Science</a> - Suitable for undergraduate introductions to logic and
early graduate courses on logic.</li>
<li><a href="https://www.springer.com/gp/book/9781447141280">Ben-Ari.
Mathematical Logic for Computer Science</a> - Semantic tableaux are used
because they are theoretically sound and easy to understand.</li>
<li><a href="https://pimbook.org/">Jeremy Kun. A Programmer’s
Introduction to Mathematics</a> - Uses your familiarity with ideas from
programming and software to teach mathematics.</li>
<li><a href="https://www.springer.com/gp/book/9783030420772">Vince.
Foundation Mathematics for Computer Science: A Visual Approach</a> - A
range of mathematical topics to provide a solid foundation for an
undergraduate course in computer science, starting with a review of
number systems and their relevance to digital computers, and finishing
with differential and integral calculus.</li>
<li><a
href="https://www.springer.com/gp/book/9783319911540">Oberguggenberger
&amp; Ostermann. Analysis for Computer Scientists: Foundations, Methods,
and Algorithms</a> - Presents an algorithmic approach to mathematical
analysis, with a focus on modelling and on the applications of analysis.
### Lecture
Notes<a name=mathlogic_preliminaries_general_lecture_notes></a></li>
<li><a
href="https://www.math.uni.wroc.pl/~mpal/academic/2013/lecture_notes.pdf">Paluszynski.
Calculus for Computer Scientists</a> - calculus lecture notes taught for
undergrad computer science students ## TCS
Inspired<a name=mathlogic_preliminaries_tcs_inspired></a> ### Lecture
Videos
Playlists<a name=mathlogic_preliminaries_tcs_inspired_lecture_videos_playlists></a></li>
<li><a
href="https://www.youtube.com/playlist?list=PLm3J0oaFux3ZYpFLwwrlv_EHH9wtH6pnX">O’Donnell.
CS Theory Toolkit</a> - It covers a large number of the math/CS topics
that you need to know for reading and doing research in Computer Science
Theory - alternatively: <a
href="https://www.bilibili.com/video/BV1Ry4y1e7zR">bilibili</a></li>
<li><a
href="https://home.ttic.edu/~madhurt/courses/toolkit2021/index.html">Madhur
Tulsiani. Mathematical Toolkit</a> - Things prof. Madhur wish he knew in
first year of grad school.</li>
<li><a
href="https://www.tifr.res.in/~prahladh/teaching/2020-21/toolkit/">Harsha
&amp; Strivastava. Toolkit for Theoretical Computer Science. Tata
Institute</a> ### Lecture
Notes<a name=mathlogic_preliminaries_tcs_inspired_lecture_notes></a></li>
<li><a href="https://yuanz.web.illinois.edu/teaching/B609fa16/">Zhou. A
Theorist’s Toolkit. Illinois</a> - It covers a large number of the
math/CS topics that you need to know for reading and doing research in
Computer Science Theory.</li>
<li><a href="https://www.cs.cmu.edu/~odonnell/toolkit13/">O’Donnell. A
Theorist’s Toolkit. CMU</a> - It covers a large number of the math/CS
topics that you need to know for reading and doing research in Computer
Science Theory.</li>
<li><a
href="https://www.cs.princeton.edu/courses/archive/fall07/cos597D/Site/lectopics.html">Arora.
Thinking Like a Theorist. Princeton</a> - It covers a large number of
the math/CS topics that you need to know for reading and doing research
in Computer Science Theory.</li>
<li><a
href="https://www.cs.princeton.edu/courses/archive/fall02/cs597D/">Arora.
A Theorist’s Toolkit. Princeton</a> - 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.</li>
<li><a
href="https://ocw.mit.edu/courses/18-409-topics-in-theoretical-computer-science-an-algorithmists-toolkit-fall-2009/">Kelner.
Topics in Theoretical Computer Science: An Algorithmist’s Toolkit.
MIT</a> - It covers a collection of geometric techniques that apply
broadly in modern algorithm design.</li>
<li><a
href="https://www.cs.purdue.edu/homes/hmaji/teaching/Spring%202023/CS-58500-Spring-2023.html">Maji
&amp; Valiant. Theoretical Computer Science Toolkit. Purdue</a> ##
Discrete
Mathematics<a name=mathlogic_preliminaries_discrete_mathematics></a> ###
Lecture
Notes<a name=mathlogic_preliminaries_discrete_mathematics_lecture_notes></a></li>
<li><a
href="https://www.cs.yale.edu/homes/aspnes/classes/202/notes.pdf">Aspnes.
Notes on Discrete Mathematics</a> - Fall 2017 of the Yale course CPSC
202a, Mathematical Tools for Computer Science.</li>
<li><a
href="https://www.cs.cornell.edu/courses/cs2802/2020fa/cs2802-20f-notes.html">Halpern.
CS 2802: Discrete Structures - Honors. 2020. Cornell</a> - Honors
lecture notes on discrete math - <a
href="https://www.cs.cornell.edu/courses/cs2802/2020fa/cs2802-20f-homework.html">Homework</a>
###
Books<a name=mathlogic_preliminaries_discrete_mathematics_books></a></li>
<li><a
href="https://www.wiley.com/en-us/The+Probabilistic+Method%2C+4th+Edition-p-9781119061953">Alon
&amp; Spencer. The Probabilistic Method</a> - A standard reference for
researchers in probabilistic methods in combinatorics. Shows also
connections to theoretical computer science.</li>
<li><a
href="https://www.mheducation.com/highered/product/discrete-mathematics-applications-rosen/M9780073383095.html">Rosen.
Discrete Mathematics and Its Applications</a> - A canonical discrete
math textbook, accessible for even high school students.</li>
<li><a
href="https://www.taylorfrancis.com/books/handbook-discrete-combinatorial-mathematics-kenneth-rosen-douglas-shier-wayne-goddard/e/10.1201/9781315156484">Rosen.
Handbook of Discrete and Combinatorial Mathematics</a> - A complete
survey of roughly all topics of discrete math and their relevance to
computing and communication engineering.</li>
<li><a
href="https://web.vu.lt/mif/s.jukna/EC_Book_2nd/index.html">Jukna.
Extremal Combinatorics</a> - Combinatorial techniques written largely
with an eye to their applications in TCS, and mostly in complexity</li>
<li><a href="https://www.springer.com/gp/book/9783030583750">Rosenberg
&amp; Trystram. Understand Mathematics, Understand Computing: Discrete
Mathematics That All Computing Students Should Know</a> - It endows the
reader with an operational conceptual and methodological understanding
of discrete mathematics for computing</li>
<li><a href="https://www.springer.com/gp/book/9780387941158">Gries &amp;
Schneider. A Logical Approach to Discrete Math</a> - It attempts to
change the way we teach logic to beginning students. Instead of teaching
logic as a subject in isolation, we regard it as a basic tool and show
how to use it.</li>
<li><a href="https://www.springer.com/gp/book/9783319030371">Mariconda
&amp; Tonolo. Discrete Calculus: Methods for Counting</a> - An
introduction to combinatorics, finite calculus, formal series,
recurrences, and approximations of sums. Readers will find also deep
insights into a range of less common topics rarely considered within a
single book. ###
MOOC<a name=mathlogic_preliminaries_discrete_mathematics_mooc></a></li>
<li><a
href="https://www.coursera.org/specializations/discrete-mathematics">Introduction
to Discrete Mathematics for Computer Science. UC San-Diego</a> - Learn
the language of Computer Science. Learn the math that defines computer
science, and practice applying it through mathematical proofs and Python
code. ## Transition To Pure Rigour
Math<a name=mathlogic_preliminaries_transition_to_pure_rigour_math></a></li>
<li>Velleman. How to Prove it: A Structured Approach. - It transitions
from solving problems to proving theorems by teaching them the
techniques needed to read and write proofs. # Surveys &amp;
Monographs<a name=surveys__monographs></a></li>
<li><a
href="https://link.springer.com/book/10.1007/978-3-319-22156-4">Sommaruga
&amp; Strahm. Turing’s Revolution: The Impact of His Ideas about
Computability</a> - A collection of historical, technical and
philosophical papers.</li>
<li><a
href="https://mitpress.mit.edu/9780262045308/ideas-that-created-the-future/">Harry
Lewis. Ideas That Created the Future: Classic Papers of Computer
Science</a> - Classic papers by thinkers ranging from Aristotle and
Leibniz to Norbert Wiener and Gordon Moore that chart the evolution of
computer science.</li>
<li><a
href="https://www.researchgate.net/profile/Lance-Fortnow/publication/220530495_A_Short_History_of_Computational_Complexity/links/0deec52bd7ab603fef000000/A-Short-History-of-Computational-Complexity.pdf">Fortnow
&amp; Homer. A Short History of Computational Complexity</a> - A
historical overview of computational complexity.</li>
<li><a href="http://www.wisdom.weizmann.ac.il/~oded/sst.html">Goldreich.
Providing Sound Foundations for Cryptography: On the Work of Shafi
Goldwasser and Silvio Micali</a> - It explains the remarkable work of
Shafi and Silvio and their works’ implications on foundations of
cryptography.</li>
<li><a
href="https://rd.springer.com/book/10.1007/978-3-540-85221-6">Building
Bridges I</a>, <a
href="https://link.springer.com/book/10.1007/978-3-662-59204-5">Building
Bridges II</a>, <a
href="https://link.springer.com/book/10.1007/978-3-642-13580-4">Fete of
Combinatorics and Computer Science</a> - Collected works in celebration
of Laszlo Lovasz, Connecting discrete math with computer science.</li>
<li><a
href="https://link.springer.com/book/10.1007/978-3-540-32777-6">Entropy,
Search, Complexity</a> - A collection of survey papers in the fields of
entropy, search and complexity. It includes also search theory and
quantum source coding. Recommended to both researchers and
students.</li>
<li><a href="https://arxiv.org/abs/0804.3401">Watrous. Quantum
Computational Complexity</a> - A survey on the intersection of
computational complexity and quantum computing.</li>
<li><a href="https://www.nature.com/articles/nature23458">Harrow &amp;
Ashley Montanaro. Quantum Computational Supremacy</a> - A remarkable
paper on quantum supremacy.</li>
<li><a
href="https://dl.acm.org/doi/abs/10.1145/1283920.1283938?casa_token=I0zkxGUh7zoAAAAA:qUozzyaWcba56dq5q6CXVo2JvGVOzcXY2J8HKoI-OZxEbxhVXlQlWF0pg5ttfs8X8pznp5j7Bbrktg">Cook.
An Overview of Computational Complexity</a> - A historical overview of
computational complexity. # Live Content<a name=live_content></a> ##
Conferences, Workshops, Events, and
Talks<a name=live_content_conferences_workshops_events_and_talks></a>
###
Aggregators<a name=live_content_conferences_workshops_events_and_talks_aggregators></a></li>
<li><a href="https://cstheory-events.org/">CS Theory Events
Aggregator</a> - An aggregator for CS theory workshops and schools.</li>
<li><a href="https://dmatheorynet.blogspot.com/">Theory
Announcements</a> - DMANET spreads information on conferences,
workshops, seminars etc. relating to discrete mathematics and
algorithms.</li>
<li><a href="http://www.nicolas-hermann.net/conf.html">Conferences in
TCS</a> - TCS Conferences collected in one table. ###
Live<a name=live_content_conferences_workshops_events_and_talks_live></a></li>
<li><a href="https://www.youtube.com/user/TCSplusSeminars">TCS+</a> - A
series of online seminars in theoretical computer science. The goal is
to make engaging talks accessible to the widest possible audience.</li>
<li><a
href="https://www.dcs.warwick.ac.uk/~igorcarb/complexity-meetings.html">Oxford-Warwick
Complexity Meetings</a> - Online informal talks dedicated to topics of
interest in computational complexity theory and related areas. The goal
is to serve as a forum for discussion and quick dissemination of
results.</li>
<li><a href="https://simons.berkeley.edu/programs">Simons’ Public
Lectures</a> - Programs, Events, and workshops, that aim toward
maximizing impact and engagement across the theoretical computer science
community.</li>
<li><a
href="https://www.youtube.com/channel/UCWFp4UWNiOv71j0sPbdNiqw">CMU
Theory</a> - Aims for a mathematical understanding of fundamental issues
in Computer Science, and to use this understanding to produce better
algorithms, protocols, and systems, as well as identify the inherent
limitations of efficient computation. ###
Archived<a name=live_content_conferences_workshops_events_and_talks_archived></a></li>
<li><a
href="https://www.youtube.com/playlist?list=PLIgnAi1_K6YGqJQVtx2EowWXwVAnU24x3">CCC
22</a> | <a href="https://eatcs.org/index.php/conferences">ICALP</a> |
<a
href="https://www.youtube.com/playlist?list=PLgKuh-lKre13VY4AJCOK5Nx8ghscvpGrE">ITCS
2022</a> | <a
href="https://www.youtube.com/playlist?list=PLgKuh-lKre11M2GCKkTySipbMIxXqKzkE">ITCS
2022 Pre-recorded</a> | <a
href="https://www.youtube.com/playlist?list=PLXjzCrdVznQKezRe9V4feOaTF95wdLyNN">STOC
2021</a> | <a
href="https://www.youtube.com/channel/UClrteoQ-ULzlZZaWi6c6iKw">FOCS
2021</a> | <a
href="https://www.youtube.com/playlist?list=PLgKuh-lKre12JHHIMp3EHQSO8Z_62TnNQ">ITCS
2021</a> | <a
href="https://www.youtube.com/playlist?list=PLn0nrSd4xjjadfcMd5xvmJ_GNSLDi1ATn">STOC
2020</a> | <a
href="https://www.youtube.com/channel/UCSpAMJ70rngnq9_jkxXJvoQ/playlists">FOCS
2020</a>.</li>
<li><a href="https://amturing.acm.org/lectures.cfm">Turing Lectures.
ACM</a></li>
<li>ACM A.M. Turing Laureate Interview. Berkeley - Interviews with
Berkeley’s Turing award winners. - <a
href="https://www.youtube.com/watch?v=yBRbSzPgG4A">Shafi Goldwasser and
Silvio Micali</a></li>
<li>Berkeley in the 80s - Interviews with eminent figures in Berkeley’s
theoretical computer science. - <a
href="https://www.youtube.com/watch?v=7D4hIe7M4qU&amp;t=1s">Shafi
Goldwasser</a></li>
<li><a
href="https://www.youtube.com/playlist?list=PLgKuh-lKre134Psz9KECgjuwJ47l3IvqW">Simons’
Theory Shorts</a> - Short accessible videos which populate theory of
computation.</li>
<li>Lex Podcast - Informal discussions podcast. - <a
href="https://www.youtube.com/watch?v=2BdBfsXbST8">Donald Knuth 1</a> |
<a href="https://www.youtube.com/watch?v=EE1R8FYUJm0">Donald Knuth 2</a>
| <a href="https://www.youtube.com/watch?v=zNdhgOk4-fE">Silvio
Micali</a> | <a
href="https://www.youtube.com/watch?v=KllCrlfLuzs">Richard Karp</a> | <a
href="https://www.youtube.com/watch?v=uX5t8EivCaM">Scott Aaronson 1</a>
| <a href="https://www.youtube.com/watch?v=nAMjv0NAESM">Scott Aaronson
2</a></li>
<li><a
href="https://www.youtube.com/channel/UCzBw287tly0c2lE6a-9XymA">Computational
Complexity</a> - Collection of workshops.</li>
<li><a
href="https://www.youtube.com/playlist?list=PLUxf7X3NCfHNqOFlnnX9nSTENSzZoOLVp">Chi-Ning
Chou. What is Computation? From Turing Machines to Black Holes and
Neurons</a> (see also <a
href="https://cnchou.github.io/mini-course/">main website</a>) -
Accessible mini-course that introduces high-level stories of TCS and its
intersections with other fields. ## Magazines, News, and
Monographs<a name=live_content_magazines_news_and_monographs></a></li>
<li><a href="https://eatcs.org/index.php/on-line-issues">EATCS
Bulletin</a> - Surveys, tutorials, conferences reports, events, open
problems and solutions, PhD Theses, and entertaining contributions.</li>
<li><a href="https://dl.acm.org/loi/sigact">SIGACT News</a> - ACM’s
official theoretical computer science news feed.</li>
<li><a href="https://www.nowpublishers.com/TCS">Foundations and Trends
in Theoretical Computer Science</a> - It provides monographs written by
leaders that give tutorial coverage of subjects, research retrospectives
as well as survey papers that offer state-of-the-art reviews fall within
the scope of the journal.</li>
<li><a
href="https://www.quantamagazine.org/tag/computational-complexity">Quanta
Magazine</a> - Features breakthroughs in the field, written in an
accessible style for non-experts. ## Blogs
Aggregators<a name=live_content_blogs_aggregators></a></li>
<li><a href="https://theory.report/">Theory of Computing Blog
Aggregator</a> - A blog Aggregator for all blogs related to TCS. ##
Jobs<a name=live_content_jobs></a> ###
Aggregator<a name=live_content_jobs_aggregator></a></li>
<li><a href="https://cstheory-jobs.org">CS Theory Jobs</a> - TCS Jobs
announcements.</li>
<li><a href="https://sites.google.com/view/tcsjobmarket/home">TCS Job
Market</a> - Theoretical Computer Science (TCS) job candidates,
including PhD students expecting to graduate by Sep 1, 2023, current
postdocs, and current faculty. ###
Lists<a name=live_content_jobs_lists></a></li>
<li><a href="http://grigory.us/blog/theory-jobs-2022/">Yaroslavtsev.
Hires spreadsheet 2022</a> - A crowdsourced spreadsheet created to
collect information about theory hires in year 2022.</li>
<li><a
href="https://www.cs.princeton.edu/~smattw/masters/masters.html">Rubinstein
&amp; Weinberg. Research Masters in TCS</a> - A list of master programs
in TCS.</li>
<li><a
href="https://docs.google.com/spreadsheets/d/1znVmERIVm2Fz2cbYjXE97VH9eIx7UdaLh5n3FBYb8o8/edit?usp=sharing">Barak.
Research Masters in TCS</a> - A list of master programs in TCS. ##
Online Communities<a name=live_content_online_communities></a></li>
<li><a href="https://cstheory.stackexchange.com/">TCS Stack Exchange</a>
- Stack Exchange (QnA) of theoretical computer science.</li>
<li><a href="https://www.reddit.com/r/theoreticalcs">TCS Subreddit</a>-
Theoretical computer science’s subreddit. # Other
Resources<a name=other_resources></a> ## Blog Posts and
Essays<a name=other_resources_blog_posts_and_essays></a></li>
<li><a
href="https://omereingold.wordpress.com/cs-163-the-practice-of-theory-research/">Omer
Reingold. The Practice of Theory Research</a> - A research methods
course, concentrating on the how rather than the what. It focuses on
research practices common for computer science theory research.</li>
<li><a
href="https://theorydish.blog/2021/04/15/toc-a-personal-perspective-2021/">Omer
Reingold. TOC: a Personal Perspective (2021)</a> - In celebration of 25
years for “TOC: a Scientific Perspective (1996),” by Oded Goldreich and
Avi Wigderson. It spots the light on a criticism directed to TCS, that
it is not as deep as Math and not as useful as CS.</li>
<li><a href="https://www.cs.cmu.edu/~mblum/research/pdf/grad.html">Blum.
You and Your Research: An Advice to a Beginning Graduate Student</a> -
Manuel Blum, A very popular figure in TCS, gives research advices for
juniors.</li>
<li><a
href="https://link.springer.com/chapter/10.1007%2F978-1-4612-5695-3_58">Dijkstra.
The Three Golden Rules for Successful Scientific Research</a> - A note
devoted to three rules that must be followed if you want to be
successful in scientific research.</li>
<li><a
href="http://www.wisdom.weizmann.ac.il/~oded/essays.html">Goldreich.
Essays and Opinions</a> - Personal Essays by Oded Goldreich. They are
very unique in their conceptual message of TCS and its community.</li>
<li><a
href="https://windowsontheory.org/2015/11/03/advice-for-the-budding-theorist/">Barak.
Advice for The Budding Theorist</a> - Tips for anyone interested in
theoretical computer science.</li>
<li><a href="https://thmatters.wordpress.com/surveys/">Barak. Surveys
For Students</a> - Surveys for high-school, undergraduate, and even
researchers.</li>
<li><a href="https://www.boazbarak.org/informal/">Barak. Non-technical
or Less-technical Writings and Talks</a> - Posts oriented more for a
less-technically matured audience.</li>
<li><a
href="https://rjlipton.wpcomstaging.com/2022/01/26/a-list-of-most-theory-blogs/">Lipton
&amp; Regan</a> - A list of theory blogs for computer science.</li>
<li><a
href="https://www2.eecs.berkeley.edu/bears/CS_Anniversary/karp-talk.html">Karp.
A Personal View of Computer Science at Berkeley</a> - Karp addresses: In
1968 computer science at Berkeley was problematic, with two departments
working independently to develop programs, and his personal
reflections.</li>
<li><a
href="https://www.cs.virginia.edu/~robins/YouAndYourResearch.html">Hamming.
You and Your Research</a> - Why do so few scientists make significant
contributions and so many are forgotten in the long run? The talk is
about what Hamming has learned.</li>
<li><a href="https://www.nature.com/articles/426389a">Weinberg. Four
Golden Lessons</a> - Lessons for students and researchers given by
Steven Weinberg.</li>
<li><a
href="http://assets.press.princeton.edu/chapters/gowers/gowers_VIII_6.pdf">Princeton’s
Companion. Advice to a Young Mathematician</a> - Five contributors draw
on their experiences of mathematical life and research, and to offer
advice that they might have liked to receive when they were just
setting-out on their careers.</li>
<li><a href="https://terrytao.wordpress.com/career-advice/">Terry.
Career Advice</a> - A collection of various pieces of advice on academic
career issues in mathematics, roughly arranged by the stage of career at
which the advice is most pertinent.</li>
<li><a
href="https://igorpak.wordpress.com/2022/10/26/how-to-start-a-paper/">Igor
Pak. How to Start a Paper</a> - Why should you introduce a conceptual
preliminary motivating the story of your paper. ## Special Magazines and
Workshops<a name=other_resources_special_magazines_and_workshops></a></li>
<li><a href="https://dl.acm.org/toc/xrds/2012/18/3">The Legacy of Alan
Turing: Pushing the Boundaries of Computation (Volume 18, Issue 3,
Spring 2012). ACM, XRDS</a> - ACM’s students magazine special issue for
theory of computation.</li>
<li><a
href="https://cra.org/ccc/wp-content/uploads/sites/2/2021/07/TCS-Visioning-Workshop-Report-2020.pdf">TCS
Visioning Workshop 2020 Report</a> - Recent accomplishments and new
challenges in the field of TCS. ## Popular Science
Books<a name=other_resources_popular_science_books></a></li>
<li><a href="https://goldenticket.fortnow.com">Fortnow. The Golden
Ticket: P, NP, and the Search for the Impossible</a> - A nontechnical
introduction to P-NP, its rich history, and its algorithmic implications
for everything we do with computers and beyond.</li>
<li><a
href="https://link.springer.com/book/10.1007/978-3-319-62680-2">Ausiello.
The Making of a New Science: A Personal Journey Through the Early Years
of Theoretical Computer Science</a> - A story about people, pioneers
with diverse backgrounds and characters who established a new
field.</li>
<li><a
href="https://assets.cambridge.org/97805211/99568/frontmatter/9780521199568_frontmatter.pdf">Aaronson.
Quantum Computing Since Democritus</a> - It covers an amazing array of
topics. Beginning in antiquity with Democritus, it progresses through
logic and set theory,computability and complexity theory, quantum
computing, cryptography, the information content of quantum states, and
the interpretation of quantum mechanics.</li>
<li><a
href="http://www.daviddeutsch.org.uk/books/the-fabric-of-reality/">Deutsch.
The Fabric of Reality: The Science of Parallel Universes and Its
Implications</a> - 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.</li>
<li><a
href="https://mitpress.mit.edu/books/turing-novel-about-computation">Papadimitriou.
Turing: A Novel About Computation</a> - The world of computation
according to Turing, an interactive tutoring program, as told to
star-crossed lovers: a novel.</li>
<li><a
href="https://link.springer.com/book/10.1007/978-3-662-05642-4">Teuscher.
Alan Turing: Life and Legacy of a Great. Springer</a> - Essays which
spans the entire rich spectrum of Turing’s life, research work and
legacy.</li>
<li><a href="http://www.charlespetzold.com/AnnotatedTuring/">Petzold.
The Annotated Turing: A Guided Tour Through Alan Turing’s Historic Paper
on Computability and the Turing Machine</a> - A Guided Tour through Alan
Turing’s Historic Paper on Computability and the Turing Machine.</li>
<li><a href="https://www.springer.com/gp/book/9780387982694">Shasha
&amp; Lazere. Out of their Minds: The Lives and Discoveries of 15 Great
Computer Scientists</a> - Interviews with era’s greatest scientists
about their inspirations, discoveries, and personal interests. ## Cheat
Sheets<a name=other_resources_cheat_sheets></a></li>
<li><a
href="https://www.cosy.sbg.ac.at/~held/teaching/aads/TCS-cheat_sheet.pdf">TCS
Cheat Sheet</a> - A sheet of notes containing essential toolboxes needed
by any theoretical computer scientist. ## Network
Groups<a name=other_resources_network_groups></a></li>
<li><a href="https://www.sigact.org/articles/web_pages.html">SIGACT</a>
- Info page of ACM’s Special Interest Group on Algorithms and
Computation Theory.</li>
<li><a href="https://polytcs.wordpress.com/">PolyTCS</a> - A project
which promotes massive collaborations to solve theoretical computer
science problems.</li>
<li><a
href="https://sites.google.com/view/complexitynetwork/home">Complexity
Network</a> - Hosts collaboration between the three computational
complexity groups at Imperial College London, University of Oxford and
University of Warwick. It promotes smooth flow of ideas between the
three groups and beyond.</li>
<li><a
href="https://cstheory.blogoverflow.com/theoretical-computer-science-conferences-and-workshops/">List
of TCS Conferences and Workshops</a> - A list of conferences and
workshops in theoretical computer science. # Related Awesome
Lists<a name=related_awesome_lists></a></li>
<li><a
href="https://github.com/tayllan/awesome-algorithms">Algorithms</a> -
Awesome list for design and analysis of algorithms</li>
<li><a href="https://github.com/rossant/awesome-math">Mathematics</a> -
Awesome list for freely available lecture notes on mathematics</li>
<li><a
href="https://github.com/sobolevn/awesome-cryptography">Cryptography</a>
- Awesome list for cryptography</li>
<li><a
href="https://github.com/desireevl/awesome-quantum-computing">Quantum
Computing</a> - Awesome list for quantum computing</li>
<li>P.S. See also <a href="https://github.com/ossu/math">Math</a> and <a
href="https://github.com/ossu/computer-science">CS</a> curricula by <a
href="https://github.com/ossu">Open Source Society University</a></li>
</ul>