Watch this! - this - this.
[I'm trying to revamp this thing, so let's go.]
- Online Course: MIT - Real Analysis OCW
- Book: David J. McCay - Information Theory, Inference and Learning Algorithms
- Online Course (resources only): Stanford University - EE 376A
- Book: Bob Coecke and Aleks Kissinger - Picturing Quantum Processes
- Papers: The ones suggested in this videp
- CMU - Statistical Machine Learning.
- CMU - Convex Optimization.
- CMU - Foundations of Machine Learning and Data Science.
- Princeton - Convex and Conic Optimization.
- Cornell - Machine Learning Theory.
- Cornell - Spectral Graph Theory.
- Stanford - Convex Optimization I.
- Boaz Barak - An Intensive Introduction to Cryptography.
- Roger Grosse - Topics in Machine Learning: Neural Net Training Dynamics.
- Jimmmy Ba - Neural Networks and Deep Learning.
- Akshay Krishnamurthy - Machine Learning Theory.
- Matus Telgarsky - Deep Learning Theory.
- Percy Liang - Statistical Learning Theory.
- Harvard - New Technologies in Mathematics Seminar Series.
- Kevin Buzzard - The Future of Mathematics?
- Formalizing Mathematics - GitHub - WordPress
- Bo’az Klartag: On Yuansi Chen’s work on the KLS conjecture I.
- Illustrating Mathematics.
- Stanford - Machine Learning Systems Design.
- JHU's advanced data science.
- Winter School on "The Interplay between High-Dimensional Geometry and Probability".
- Coding Theory and Applications.
- Modern Discrete Probability.
- Stephen Boyd and Lieven Vandenberghe - Convex Optimization.
- Michael Sipser - Introduction to the Theory of Computation.
- Ming Li and Paul Vitanyi - An Introduction to Kolmogorov Complexity and Its Applications.
- Christos Papadimitriou - Computational Complexity.
- Christopher Bishop - Pattern Recognition and Machine Learning.
- John von Neumann - Theory of Self-Reproducing Automata.
- Understanding and Using Linear Programming.
- Oded Goldreich - The Foundations of Cryptography.
- A Graduate Course in Applied Cryptography.
- Theodore G. Faticoni - The Mathematics of Infinity.
- Graphical Models in a Nutshell.
- Computing Arbitrary Functions of Encrypted Data.
- On Universal Prediction and Bayesian Confirmation.
- A Mathematical Theory of Communication.
- Communication Theory of Secrecy Systems.
- Algorithms, Games, and the Internet.
- Knowledge, Understanding, and Computational Complexity.
- The Surprise Examination Paradox and the Second Incompleteness Theorem.
- A Computer Scientist's View of Life, the Universe, and Everything.
- The Turing Test As Interactive Proof.
- The History and Status of the P versus NP Question.
- The Problem of Logical Omniscience I.
- Computing Machinery and Intelligence.
- On Computable Numbers.
- A Theory of the Learnable.
- Evolvability.
- P, NP and Mathematics.
- Knowledge, Creativity, and P versus NP.
- Weak quasi-factorization for the Belavkin-Staszewski relative entropy.
- Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication.
- Doing Research.
- What precisely Is “Categorification”?.
- Topology 101: The Hole Truth.
- Higher-order Network Analysis Takes Off, Fueled by Old Ideas and New Data.
- Mathematicians Resurrect Hilbert’s 13th Problem.
- ML Theory with bad drawings.
- Statistics of Basketball Scoring and Lead Changes by Sidney Redner.
- https://mathworld.wolfram.com/SchanuelsConjecture.html.