, Mathematics for Artificial
Intelligence
Artificial intelligence (AI) and machine learning (ML) are rapidly growing fields,
drawing great interest among students. Many students in a range of fields, includ-
ing 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 experiences. 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 cal-
culus course. Linear algebra is presented as needed and should not require a com-
pleted 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. Exer-
cises are included.
,Textbooks in Mathematics
Series editors:
Al Boggess, Kenneth H. Rosen
Exploring Linear Algebra, Second Edition
Labs and Projects with Mathematica•
Crista Arangala
Measure Theory and Fine Properties of Functions, Second Edition
Lawrence Craig Evans and Ronald F. Gariepy
Set Theory
An Introduction to Axiomatic Reasoning
Robert André
Introduction to Differential and Difference Equations Through Modeling
William P. Fox and Robert E. Burks
Abstract Algebra, Third Edition
An Interactive Approach
William Paulsen
Elements of Algebraic Topology, Second Edition
James R. Munkres, Steven G. Krantz, and Harold R. Parks
One Complex Variable from the Several Variable Point of View
Peter V. Dovbush and Steven G. Krantz
Math Anxiety How to Beat It
Brian Cafarella
Lectures on Differential Geometry with Maple
Mayer Humi
Numerical Analysis for Engineers
Methods and Applications
Bilal M. Ayyub and Richard H. McCuen
An Invitation to Real Analysis
Andrew D. Hwang
Fourier Series and Boundary Value Problems with Engineering Applications
Youssef N. Raffoul
A Course in Real Analysis, Second Edition
Hugo D. Junghenn
Real and Functional Analysis
Kenneth Kuttler
Mathematics for Artificial Intelligence
Jane Hawkins
https://www.routledge.com/Textbooks-in-Mathematics/book-series/CANDHTEX-
BOOMTH
, Mathematics for Artificial
Intelligence
Jane Hawkins
Intelligence
Artificial intelligence (AI) and machine learning (ML) are rapidly growing fields,
drawing great interest among students. Many students in a range of fields, includ-
ing 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 experiences. 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 cal-
culus course. Linear algebra is presented as needed and should not require a com-
pleted 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. Exer-
cises are included.
,Textbooks in Mathematics
Series editors:
Al Boggess, Kenneth H. Rosen
Exploring Linear Algebra, Second Edition
Labs and Projects with Mathematica•
Crista Arangala
Measure Theory and Fine Properties of Functions, Second Edition
Lawrence Craig Evans and Ronald F. Gariepy
Set Theory
An Introduction to Axiomatic Reasoning
Robert André
Introduction to Differential and Difference Equations Through Modeling
William P. Fox and Robert E. Burks
Abstract Algebra, Third Edition
An Interactive Approach
William Paulsen
Elements of Algebraic Topology, Second Edition
James R. Munkres, Steven G. Krantz, and Harold R. Parks
One Complex Variable from the Several Variable Point of View
Peter V. Dovbush and Steven G. Krantz
Math Anxiety How to Beat It
Brian Cafarella
Lectures on Differential Geometry with Maple
Mayer Humi
Numerical Analysis for Engineers
Methods and Applications
Bilal M. Ayyub and Richard H. McCuen
An Invitation to Real Analysis
Andrew D. Hwang
Fourier Series and Boundary Value Problems with Engineering Applications
Youssef N. Raffoul
A Course in Real Analysis, Second Edition
Hugo D. Junghenn
Real and Functional Analysis
Kenneth Kuttler
Mathematics for Artificial Intelligence
Jane Hawkins
https://www.routledge.com/Textbooks-in-Mathematics/book-series/CANDHTEX-
BOOMTH
, Mathematics for Artificial
Intelligence
Jane Hawkins
