How to Think about Algorithms (Paperback)

Jeff Edmonds

ISBN 10: 1009302132 / ISBN 13: 9781009302135

Verlag: Cambridge University Press, Cambridge, 2024

Beschreibung

Paperback. 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. The second edition of this student-friendly textbook now includes over 150 new exercises, key concept summaries and a chapter on machine learning algorithms. Its approachability and clarity make it ideal as both a main course text or as a supplementary book for students who find other books challenging. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Bestandsnummer des Verkäufers 9781009302135

Bibliografische Details

• Titel: How to Think about Algorithms (Paperback)
• Verlag: Cambridge University Press, Cambridge
• Erscheinungsjahr: 2024
• Sprache: Englisch
• ISBN-10: 1009302132
• ISBN-13: 9781009302135
• Einband: Paperback
• Auflage: 2. Auflage
• Zustand: new

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

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