# ML Reference Resources

Textbooks and courses that I have not consumed yet, but here for reference.

I have not read these books at all, but keeping here as good (potentially introductory) references to complex topics when I need them.

## General Mathematics

- Kennethsite
- The Fourier Transform: A Tutorial Introduction by James V Stone (2021)
- Introductory Functional Analysis with Applications by Erwin Kreyszig (1989)
- Visual Differential Geometry and Forms: A Mathematical Drama in Five Acts by Tristan Needham (2021)
- An Elementary Introduction to Information Geometry
- Methods of Information Geometry by Hiroshi Nagaoka and Shun-Ichi Amari (2007)
- Encyclopedia of Distances by Elena Deza, Michel Marie Deza (2009)

- All the mathematics you missed (but need for graduate school) by Thomas A. Garrity (2021)
- Mathematical Methods in the Physical Sciences by Mary L. Boas (2005)
- The Physics of Information Technology by Neil Gershenfeld (2011)
- The Fourier Transforms and its Applications by Brad Osgood (2008)
- Nonlinear Programming by Dimitri P. Bertsekas (2016)
- Physically Based Rendering by Matt Pharr, Wenzel Jakob, and Greg Humphreys (2023)
- A Gentle Introduction to the Art of Mathematics by Joe Fields (2023)

### Numerical Methods

- Applied Numerical Linear Algebra by William W. Hager (2021)
- Probabilistic Numerics by Philipp Hennig, Michael A. Osborne, Hans Kersting (2022)
- Numerical Methods that Work by Forman S. Acton (1970)
- Matrix Tricks for Linear Statistical Models: Our Personal Top Twenty by Simo Puntanen, George P. H. Styan, Jarkko Isotalo (2011)

### Differential Equations

- Differential Equations. By Blanchard, Devaney (2011)

- Stochastic Processes and Applications: Diffusion Processes, the Fokker-Planck and Langevin Equations by Grigorios A. Pavliotis (2014)
- An Introduction to the Numerical Simulation of Stochastic Differential Equations by Desmond Higham and Peter Kloeden (2021)
- Applied Stochastic Analysis by Weinan E, Tiejun Li, and Eric Vanden-Eijnden (2019)

- Stochastic Methods: A Handbook for the Natural and Social Sciences by Crispin Gardiner (2009)
- Stochastic Processes for Physicists: Understanding Noisy Systems by Kurt Jacobs (2010)
- A Primer on PDEs: Models, Methods, Simulations by Sandro Salsa , Federico M. G. Vegni , Anna Zaretti , Paolo Zunino (2013)
- The (Unfinished) PDE Coffee Table Book by Lloyd N. Trefethen and Kristine Embree (2001)

### Signal Processing

- An Introduction to Statistical Signal Processing by Robert M. Gray and Lee D. Davisson (2004)

## Statistics

- [Introduction to Modern Statistics](https://www.openintro.org/book/ims/

- All of Statistics by Larry Wasserman (2010)

- Bayesian Statistics for Experimental Scientists: A General Introduction Using Distribution-Free Methods by Richard A. Chechile (2020)

- Causal Inference for Statistics, Social, and Biomedical Sciences
- Statistical Inference by George Casella and Roger L. Berger (1990)
- Principles of Uncertainty by Joseph B. Kadane (2020)

- Introduction to Mathematical Statistics by by Robert Hogg, Joseph McKean, Allen Craig (2012)
- Causal Inference in Statistics - A Primer by Judea Pearl (2016)
- Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos (2021)
- Advanced Data Analysis from an Elementary Point of View by Cosma Rohilla Shalizi
- Mathematical Statistics with Applications by John E. Freund (2014)
- Bayesian Statistics for the Social Sciences by David Kaplan (2023)
- The Book of Statistical Proofs by Joram Soch et. al. (2024)

## Machine Learning

- Kernel Methods in Machine Learning by Julien Mairal (2021)
- Random Matrix Methods for Machine Learning by Romain Couillet and Zhenyu Liao (2022)
- Information Theory: From Coding to Learning by Yury Polyanskiy and Yihong Wu (2023)
- The Elements of Differentiable Programming by Mathieu Blondel and Vincent Roulet (2024)

### Learning Theory

- Mathematics and Computation by Avi Wigderson (2019)
- Concentration Inequalities by Aditya Gopalan and Himanshu Tyagi (2021)
- Generalization Bounds: Perspectives from Information Theory and PAC-Bayes by Fredrik Hellström, Giuseppe Durisi, Benjamin Guedj, Maxim Raginsky (2023)

### Time Series

- Time Series Analysis by State Space Methods by James Durbin and Siem Jan Koopman (2012)
- Bayesian Inference of State Space Models by Kostas Triantafyllopoulos (2021)

### RL/Control

- Data-Driven Science & Engineering: Machine Learning, Dynamical Systems and Control by Steven L. Brunton and J. Nathan Kutz (2019)
- Introduction to Markov Decision Processes by Martin L. Puterman and Timothy C. Y. Chan (2021)
- Algorithms for Decision Making by Mykel J. Kochenderfer, Tim A. Wheeler, Kyle H. Wray (2022)

## Problem Books

- Problems In Linear Algebra by I. V. Proskuryakov (1978)
- Linear Algebra Problem Book by Paul R. Halmos (1995)