AI and Math Books

Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares

This book is used as the textbook for our own courses ENGR108 (Stanford) and EE133A (UCLA), where you will find additional related material.

By Boyd and Vandenberghe


Convex Optimization 

By Boyd and Vandenberghe


Understanding Machine Learning: From Theory to Algorithms

By Shai Shalev-Shwartz and Shai Ben-David


Mathematics for Machine Learning

By Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong.


Reinforcement Learning: An Introduction

By Richard S. Sutton and Andrew G. Barto


Optimization Models

By G.C. Calafiore and L. El Ghaoui


Machine Learning Refined

By Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos


Neuronal Dynamics

From single neurons to networks and models of cognition

By Wulfram Gerstner, Werner M. Kistler, Richard Naud and Liam Paninski


Understanding Deep Learning

By Simon J. D. Prince

Published by MIT Press Dec 5th 2023


A Brief Introduction to Neural Networks

Neural networks are a bio-inspired mechanism of data processing, that enables computers to learn technically similar to a brain and even generalize once solutions to enough problem instances are tought.

By David Kriesel


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