Artificial intelligence (AI) and machine learning (ML) are rapidly growing fields, drawing great interest among students. Many students in a range of fields, including mathematics, computer science, statistics, data science, and more, see AI and ML as the keys to their futures.
Mathematics for Artificial Intelligence provides the basic mathematics needed to understand AI and ML. It serves both students of mathematics and those who want to fill any gaps in their mathematics experience. It is written as both a text for a course and as a focused look at mathematics needed for readers hoping to learn more.
The author has taught every topic in this book, often in different contexts, and the material and exercises are drawn from lecture notes. The material in the book represents a curated set of topics from the undergraduate math curriculum, some first-year seminar material, and some student project topics. Through carefully chosen examples and discussion in the text, the reader will learn how and where these tools are applied. AI and ML connections are raised along the way.
It presumes the reader has at least completed the traditional three-semester calculus course. Linear algebra is presented as needed and should not require a completed course. The book is also well-suited for self-paced learning. Each chapter can be read independently with the help of the index for cross-referencing. Exercises are included.
Table of Contents:
1. Calculus of one variable 2. Calculus of several variables 3. Matrix Algebra 4. Probability 5. Graphs, shifts, and stochastic matrices 6. Neural networks
About the Author :
Jane Hawkins is a Professor Emerita at the University of North Carolina at Chapel Hill who has held faculty positions at Stony Brook University, Cal Tech, and Duke University, with over 50 research papers published in dynamical systems, ergodic theory, differentiable and complex dynamics, Markov shifts, and HIV and Ebola virus dynamics, and is the author of two books, Ergodic Dynamics and The Mathematics of Cellular Automata. An inaugural American Mathematical Society (AMS) Fellow, she chaired the AMS Committee on Science Policy for two years, testified before Congressional committees on the importance of science and mathematics in the U.S., and served as a Program Director in the Division of Mathematical Sciences at the National Science Foundation. Her teaching spans multivariable calculus, linear algebra, differential equations, probability theory, and dynamical systems, often using computational tools, and she has supervised 20 Ph.D. and master's students, many contributing to mathematical breakthroughs through computer-generated insights. She has delivered over 160 research talks across four continents and numerous public lectures on fractals, virus classification, and HIV dynamics, while also teaching undergraduate courses on dynamics, cellular automata, and probability in society, having received her Ph.D. from the University of Warwick in England as a Marshall Scholar.
