Machine Learning, AI & Computer Science

Machine Learning & AI

The Artificial Intelligence and Machine Learning research group at Princeton University. See also Sanjeev Arora's ICM 2018 plenary lecture on The mathematics of machine learning and deep learning.
Both April and July 2024 issues of the Bulletin of the American Mathematical Society contain a collection of papers on Math and AI.
Besides, the Notices of the AMS have published more pedagogical papers on the mathematical foundations of Machine Learning:
- Machine Learning and Invariant Theory (2023), vol 70, 1205-13,
- Mathematizing Human Perception (2023), vol 70, 1081-88,
- Mathematical Foundations of Graph-Based Bayesian Semi-Supervised Learning (2022), vol 69, 1717-29,
- Model Selection for Optimal Prediction in Statistical Machine Learning (2020), vol 67, 155-168,
- Machine Learning: Mathematical Theory and Scientific Applications (2019), vol 66, 1813-20,
- From Decoupling and Self-Normalization to Machine Learning (2019), vol 66, 1641-46,
The Spring 2020 & 2021 lectures on deep learning by Yann LeCun at the NYU Center for Data Science.
The lectures on data science by Stéphane Mallat at the Collège de France.

Computer Science Resources

Donald Knuth's home page. Don Knuth is the author of the celebrated books: The Art of Computer Programming You can find there everything important on semi-numerical and numerical algorithms, \(\TeX\) etc.
The Digital Library of Mathematical Functions of the National Institute of Standards and Technology (NIST); R. Boisvert, C.W. Clark, D. Lozier, and F. Olver wrote a presentation published in the Notices of the AMS A special functions handbook for the digital age (2011). This new online library supersedes the celebrated book by M. Abramowitz and I. Stegun: the Handbook of Mathematical Functions.
The C++ programming language is still the most powerful language. Valgrind is a useful tool for memory leak debugging, profiling, etc.
NLopt is a free/open-source library for nonlinear optimization,
The NIST Statistical Reference Datasets (NIST StRD) for assessing the numerical accuracy of statistical software packages. This database is very useful for checking parts of statistical procedures.
Useful information on the issue of numerical accuracy of statistical software packages is available at B.D. McCullough's web page.