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

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Convex Optimization 

By Boyd and Vandenberghe

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Understanding Machine Learning: From Theory to Algorithms

By Shai Shalev-Shwartz and Shai Ben-David

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Mathematics for Machine Learning

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

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Reinforcement Learning: An Introduction

By Richard S. Sutton and Andrew G. Barto

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Optimization Models

By G.C. Calafiore and L. El Ghaoui

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Machine Learning Refined

By Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos

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Neuronal Dynamics

From single neurons to networks and models of cognition

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

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Understanding Deep Learning

By Simon J. D. Prince

Published by MIT Press Dec 5th 2023

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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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