How to Think about Algorithms

Jeff Edmonds

ISBN 10: 1009302132 / ISBN 13: 9781009302135

Published by Cambridge University Pr. Mär 2024, 2024

About this Item

Neuware -Understand algorithms and their design with this revised student-friendly textbook. Unlike other algorithms books, this one is approachable, the methods it explains are straightforward, and the insights it provides are numerous and valuable. Without grinding through lots of formal proof, students will benefit from step-by-step methods for developing algorithms, expert guidance on common pitfalls, and an appreciation of the bigger picture. Revised and updated, this second edition includes a new chapter on machine learning algorithms, and concise key concept summaries at the end of each part for quick reference. Also new to this edition are more than 150 new exercises: selected solutions are included to let students check their progress, while a full solutions manual is available online for instructors. No other text explains complex topics such as loop invariants as clearly, helping students to think abstractly and preparing them for creating their own innovative ways to solve problems. 464 pp. Englisch.

Seller Inventory # 9781009302135

Bibliographic Details

• Title: How to Think about Algorithms
• Publisher: Cambridge University Pr. Mär 2024
• Publication Date: 2024
• Language: English
• ISBN 10: 1009302132
• ISBN 13: 9781009302135
• Binding: Taschenbuch
• Edition: 2nd Edition
• Condition: Neu
• Dimensions: 244W x 170H x 33D millimeters 244 millimeters width by 170 millimeters height by 33 millimeters depth
• Weight: 1047 grams

About this title

Synopsis

Understand algorithms and their design with this revised student-friendly textbook. Unlike other algorithms books, this one is approachable, the methods it explains are straightforward, and the insights it provides are numerous and valuable. Without grinding through lots of formal proof, students will benefit from step-by-step methods for developing algorithms, expert guidance on common pitfalls, and an appreciation of the bigger picture. Revised and updated, this second edition includes a new chapter on machine learning algorithms, and concise key concept summaries at the end of each part for quick reference. Also new to this edition are more than 150 new exercises: selected solutions are included to let students check their progress, while a full solutions manual is available online for instructors. No other text explains complex topics such as loop invariants as clearly, helping students to think abstractly and preparing them for creating their own innovative ways to solve problems.

About the Author

Jeff Edmonds is Professor in the Department of Electrical Engineering and Computer Science at York University, Canada.