Math for Programming

Ronald T. Kneusel

ISBN 10: 171850358X / ISBN 13: 9781718503588

Published by No Starch Press,US, US, 2025

About this Item

This book summarizes all the core mathematical topics a typical professional software engineer needs to know. In condensing the various concepts covered in an undergraduate computer science program into a single volume, it provides an excellent starting point for independent study, or a refresher for those who haven't been in a classroom for years. Early chapters cover preliminary subjects like number representation systems, set theory, and Boolean algebra, followed by a dive into the field of discrete mathematics, including functions, induction proofs, number theory, combinatorics, graphs, and trees. Later sections examine essential topics in probability, statistics, linear algebra, and calculus. Rather than confine itself to abstract theory, the book focuses on practical applications and numerical methods at the level typically encountered by working software developers. In addition, hands-on code examples in Python and C make the topics concrete.

Seller Inventory # LU-9781718503588

Bibliographic Details

• Title: Math for Programming
• Publisher: No Starch Press,US, US
• Publication Date: 2025
• Language: English
• ISBN 10: 171850358X
• ISBN 13: 9781718503588
• Binding: Paperback
• Condition: New

About this title

Synopsis

A one-stop-shop for all the math you should have learned for your programming career.

Every great programming challenge has mathematical principles at its heart. Whether you’re optimizing search algorithms, building physics engines for games, or training neural networks, success depends on your grasp of core mathematical concepts. 

In Math for Programming, you’ll master the essential mathematics that will take you from basic coding to serious software development. You’ll discover how vectors and matrices give you the power to handle complex data, how calculus drives optimization and machine learning, and how graph theory leads to advanced search algorithms.

Through clear explanations and practical examples, you’ll learn to:

• Harness linear algebra to manipulate data with unprecedented efficiency
• Apply calculus concepts to optimize algorithms and drive simulations
• Use probability and statistics to model uncertainty and analyze data
• Master the discrete mathematics that powers modern data structures
• Solve dynamic problems through differential equations

Whether you’re seeking to fill gaps in your mathematical foundation or looking to refresh your understanding of core concepts, Math for Programming will turn complex math into a practical tool you’ll use every day.

About the Author

Ronald T. Kneusel has been working with machine learning in industry since 2003 and has a PhD in machine learning from the University of Colorado, Boulder. Kneusel is the author of Practical Deep Learning, Math for Deep Learning, The Art of Randomness, How AI Works, and Strange Code (all from No Starch Press), as well as Numbers and Computers and Random Numbers and Computers (Springer).