Math for Programming

by Ronald T. Kneusel

March 2025, 450 pp.

ISBN-13:

9781718503588

Math for Programming summarizes all the core math topics a typical professional software engineer needs to know. The book condenses the various mathematics concepts covered in an undergraduate computer science program into a single volume, providing a starting point for independent study or a refresher for those who are some years removed from the classroom.

The book first covers preliminary subjects like number representation systems, set theory, and Boolean algebra. Then it dives into the field of discrete mathematics, including functions, induction proofs, number theory, combinatorics, graphs, and trees. The book also examines essential topics in probability, statistics, linear algebra, and calculus.

Rather than confine itself to abstract theory, the book focuses on practical application and numerical methods at the level typically encountered by working developers. Hands-on code examples in Python and C also make the topics concrete. Brush up on all the math you should have learned and level-up your career today.

Author Bio

Ronald T. Kneusel is a data scientist who builds deep-learning (AI) systems, as well as extensive experience with medical imaging and the development of medical devices. He earned a PhD in machine learning from the University of Colorado, Boulder, has nearly 20 years of machine learning experience in industry, and is presently pursuing deep-learning projects with L3Harris Technologies, Inc. Kneusel is also the author of Random Numbers and Computers (Springer 2018), in addition to Math for Deep Learning, Practical Deep Learning, and Strange Code—all published by No Starch Press.

Table of contents

Introduction
Chapter 1. Computers and Numbers
Chapter 2. Sets and Abstract Algebra
Chapter 3. Boolean Algebra
Chapter 4. Functions and Relations
Chapter 5. Induction
Chapter 6. Recurrence and Recursion
Chapter 7. Number Theory
Chapter 8. Counting and Combinatorics
Chapter 9. Graphs
Chapter 10. Trees
Chapter 11. Probability
Chapter 12. Statistics
Chapter 13. Linear Algebra
Chapter 14. Differential Calculus
Chapter 15. Integral Calculus
Chapter 16. Differential Equations

The chapters in red are included in this Early Access PDF.