Miscellaneous

Free Courses and Books (TCS)

• Randomized Algorithms by James Aspnes.
• Advanced Complexity Theory by Hamed Hatami.
• Computational Complexity by Manoj M. Prabhakaran.
• Communication Complexity by Mark Bun .
• Computational Social Choice by Rohit Vaish .
• Analytical Toolkit in Computer Science by Hemanta K. Maji.
• A Theorist's toolkit by Ryan O'Donnell.
• Data Stream Algorithms by Amit Chakraborti.
• Theory of Metric Embeddings by Harald Räcke.
• Lecture notes on metric embeddings by Jiří Matoušek.
• Algorithms book by Jeff Erickson .
• Introduction to Theoretical Computer Science by Boaz Barak.
• Lecture Notes by Tim Roughgarden.
• Analysis of Boolean Functions by Ryan O'Donnell.
• Analysis of Boolean Functions by Ramprasad Saptarishi
• Playlist on Analysis of Boolean Functions at the Simons Institute.
• Probabilistic Method in Combinatorics by Yufei Zhao .

Free Courses and Books (Quantum)

• Introduction to Quantum Computing by Sevag Gharibian.
• Introduction to Quantum Computing by Nathan Wiebe.
• Introduction to Quantum Information Theory by Mark M. Wilde.
• Quantum Complexity Theory by Scott Aaronson.
• Quantum Complexity Theory by Sevag Gharibian.
• Quantum Computing lecture notes by Ronald de Wolf.

Free Courses and Books (Learning Theory and Optimization)

• Sample Complexity Part 1 and Part 2: Eli Upfal.
• Machine Learning by Ronald Rivest and Mona Singh.
• Machine Learning Theory by Akshay Krishnamurthy.
• Foundations of Modern Machine Learning by Nika Haghtalab.
• Learning Theory by Ambuj Tewari.
• Machine Learning Theory by Maria Florina Balcan.
• Understanding ML by Shai Shalev-Shwartz and Shai Ben-David.
• Convex Optimization by Sébastien Bubeck.
• Optimization Algorithms by Constantine Caramanis. .
• Mathematics of Machine Learning 2019 Summer School .
• Distribution Testing by Clément Canonne.
• Applied Information Theory by Ziv Goldfeld.

Free Courses and Books (Maths)

• Introduction to Measure Theory by Claudio Landim.
• Introduction to Group Theory by Richard E Borcherds.
• Introduction to Ring Theory by Elliot Nicholson.
• Introduction to Field Theory by Elliot Nicholson.
• Martingale Theory by Artem Sapozhnikov.

Expository Resources (TCS)

• Taxonomy of Square Matrices.
• Metric Embeddings (FOCS22 workshop) .
• Five Proofs of Chernoff's Bound with Applications by Wolfgang Mulzer.
• Edmond's Blossom Algorithm by James S. Plank .
• Tossing a biased coin by Michael Mitzenmacher .
• Determining the direction of a coin's bias by William Hoza.
• Part 1 and Part 2 on Multivariate Gaussians by Chuong B. Do .
• Understanding Ladner's Theorem by Sanjoy Das.
• Reservoir Sampling: Florian Hartmann, Stephen N. Pallone .
• Morris' algorithm by Gregory Gundersen.
• The Unique Games Conjecture by Scott Aaronson.
• Hastad's switching lemma by Victor Lecomte.
• Plancherel's trick by Victor Lecomte.

Expository Resources (Quantum)

• The General Adversary Bound: A Survey by Lily Li and Morgan Shirley.
• HSP - Review and Open Problems by Chris Lomont.
• A Survey on HSP by Frédéric Wang.
• Post Quantum Cryptography by Chris Peikert.
• Quantum algorithms: A survey of applications and end-to-end complexities by Dalzell et al.
• Quantum linear systems algorithms: a primer by Dervovic et al.
• Youtube playlist on Density Matrix and Mixed States by Diego Emilio .
• QAOA literature survey by Kunal Marwaha .
• A list of Open and Solved quantum problems by IQOQI Vienna .

Expository Resources (Learning Theory)

• Understanding Optimal Transport by Alex Williams.
• Website on Algorithms With Predictions .
• Diffusion Models : Calvin Luo, Lilian Weng, CVPR 2022 Tutorial .
• Generative Flows : Lilian Weng, Yoshua Bengio.
• Learning Theory Notes by Gene Li.
• Dijkstra's in Disguise by Eric Jang.

Expository Resources (others)

• Probability Cheatsheet by William Chen and Joe Blitzstein.
• Inequalities Cheatsheet by László Kozma.
• Notes and visualizations on Linear Algebra by Kenji Hiranabe.
• The We(a)ekly Quiz by Clement Cannone .
• Expository Papers on Algebra and Number Theory by Keith Conrad.

Curated Articles (On Research)

• Advice on giving talks by Anupam Gupta.
• The Value of Science by Richard Feynman .
• The Limits of Quantum Computers by Scott Aaronson.
• Advice to PhD students by Oded Goldreich.
• 3 qualities of successful PhD students by Matt Might.
• Writing a good introduction by Jim Kurose.
• How to write a ML paper by Jakob Foerster.
• Writing Technical Articles by Henning Schulzrinne .
• The importance of stupidity in scientific research by Martin A. Schwartz .
• Presenting a Technical Talk by Nick Feamster.
• Top 10 ways to Lose your Audience .

Curated Articles (Others)

• Andrei Nikolaevich Kolmogorov by K R Parthasarathy .
• Meeting a Gorilla by Douglas Adams and Mark Carwardine .

Other Interesting Links

I have an Erdős number of 4 and a Dijkstra number of 5 (both below the average of 4.65 and 5.14 respectively).

• My CV.
• My Blog.
• BraQIIIT Lab Reading Group log.
• A curated list of courses, lecture notes, and more!.
• My Feedly Opml File.
• Curated StackExchange Links.