Computer Science Textbooks Catalogue 2025

Higher Education

Computer Science 2025 Catalogue

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Contents Machine Learning

5

AI, Natural Language Processing, Robotics, and Computer Vision Data Science

12

Network Science

12

Programming and Software Development

13

Quantum Computing

14

6 7

Computer Architecture and Organization 9 Foundations and Algorithms

Linear Algebra

10

See Our New and Forthcoming Titles on the Next Page

Key to formats

Artificial Intelligence Foundations of Computational Agents Third edition David L. Poole, Alan K. Mackworth

PB = Paperback HB = Hardback EB = eBook HE = Digital institutional subscription access from the Higher Education website CO = Digital institutional perpetual access from Cambridge Core

A comprehensive learning resource for undergraduate and graduate students, with new chapters on deep learning, causality, and social impact.

Key to formats

July 2023 254 x 177 mm 900pp HB, EB, HE 978-1-00-925819-7 Hardback £57.99 / US$74.99 X

PB = Paperback HB = Hardback EB = eBook

Visit our Higher Education website for more information, including: • Textbook listings across all subject areas • Supplementary resources for instructors and students • Affordable print and digital purchase options • Instructor examination copies cambridge.org/highereducation

The bibliographic details and title information provided in the product listings were correct at the time of going to press. Product publication dates and prices may be subject to change.

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Data Structures and Algorithms in Java

Data Structures and Algorithms in Java

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New and Forthcoming

A ProjectBased Approach

Dan S. Myers

Data Structures and Algorithms in Java A Project-Based Approach Dan S. Myers, Rollins College, Florida A hands-on undergraduate CS2 textbook covering all core data structures and algorithms topics with an emphasis on real-world projects and skills.

978-1-009-26033-6 | Hardback | £54.99/US$69.99 | 642pp | 2024

This textbook is designed for students in statistics, data science, biostatistics, engineering, and physical science programs who need a solid course in the fundamental concepts, methods and theory of statistics to understand, use, and build on modern statistical techniques for complex problems. Traditional courses on mathematical statistics often cover topics that have little relevance to some of today’s most important research challenges. This book covers more modern topics, like regression trees, large-scale hypothesis testing, bootstrapping, MCMC, time series, and fewer theoretical topics like the Cramer-Rao lower bound and the Rao-Blackwell theorem.

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Theory and methods are applied to practical problems so students see the connection between theory and applications.

More than 250 high-quality figures, 180 involving actual data.

Data and R code available on the website so students can reproduce the examples and complete hands-on exercises.

Steven E. Rigdon, Saint Louis University, Missouri Ronald D. Fricker, Jr, Virginia Polytechnic Institute and State University Douglas C. Montgomery, Arizona State University • •

“An excellent resource for students with diverse backgrounds, offering a thorough exploration of fundamental topics in statistics. The clear explanation of concepts, methods, and theory, coupled with an abundance of practical examples, provides a solid foundation to help students understand statistical principles and bridge the gap between theory and application.

This book offers invaluable insights and guidance for anyone seeking to master the principles of statistics. I highly recommend it.”

Haijun Gong, Saint Louis University

“Professors Rigdon, Fricker and Montgomery have put together an impressive volume that covers not only basic probability and basic statistics, but also includes extensions in a number of directions, all of which have immediate relevance to the work of practitioners in quantitative fields. Suffused with common sense and insights about real data and problems, it is both approachable and precise. I’m excited about the inclusion of material on power and on multiple testing, both of which will help users become smarter about what their analyses can do, and I applaud their omission of too much theory. I also appreciate their use of R and of real data. This would be an excellent text for undergraduate or graduate-level data analysts.”

Sam Buttrey, Naval Postgraduate School (NPS)

For students in statistics, data science, engineering, and science programs needing a solid course in statistical theory and methods.

ProbStatsforDS

Introduction to Probability and Statistics for Data Science with R

The authors develop the methods from both an intuitive and mathematical angle, illustrating with simple examples how and why the methods work. More complicated examples, many of which incorporate data and code in R, show how the method is used in practice. In this way, students get the big picture about how statistics works and can be applied.

RIGDON FRICKER MONTGOMERY

9781009568357 Rigdon et al. PB C M Y K

Introduction to Probability and Statistics for Data Science With R

Introduction to

Probability and Statistics for Data Science with R

For Instructors Instructor's manual PowerPoint slides and JPEGs of figures from the book For Students Data repository Code repository Selected solutions

Cover image: Yuichiro Chino/ Moment via Getty images.

Cover design by Zoe Naylor.

978-1-00956-835-7 | Paperback | £67.99/US$89.99 | 828pp | 2025

Parallel Computer Organization and Design Second Edition Michel Dubois, formerly University of Southern California Murali Annavaramm, University of Southern California Per Stenstrom, Chalmers University of Technology Design-oriented text for advanced computer architecture courses, with a streamlined structure and new material on GPUs and memory systems.

978-1-009-44759-1 | Hardback | c.£64.99/c.US$89.99 | c.700pp | 2025

Formal Methods, Informally Writing Programs that Work Carroll Morgan, University of New South Wales An undergraduate textbook showing how to use formal methods informally, without formal logic, to write and maintain computer programs.

978-1-009-42102-7 | Hardback | c.£39.99/c.US$49.99 | c.350pp | 2025 4

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STEVEN E. RIGDON RONALD D. FRICKER JR. DOUGLAS C. MONTGOMERY

Machine Learning

A Hands-On Introduction to Machine Learning

The Science of Deep Learning

Dive into Deep Learning

Iddo Drori

Chirag Shah

Up-to-date guide to deep learning with unique content, rigorous math, unified notation, comprehensive algorithms, and high-quality figures.

Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola

A self-contained and practical introduction that assumes no prior knowledge of programming or machine learning.

December 2022 253 x 203 mm 500pp HB, EB, HE 978-1-00-912330-3 Hardback £46.99 / US$59.99 X

August 2022 244 x 170 mm 360pp HB, EB, HE 978-1-108-83508-4 Hardback £44.99 / US$59.99 P

An approachable text combining the depth and quality of a textbook with the interactive multi-framework code of a hands-on tutorial.

January 2024 253 x 203 mm 574pp PB, CO 978-1-00-938943-3 Paperback £24.99 / US$29.99 P

Machine Learning A First Course for Engineers and Scientists Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, Thomas B. Schön

Presents carefully selected supervised and unsupervised learning methods from basic to state-of-the-art,in a coherent statistical framework.

March 2022 253 x 177 mm 350pp HB, EB, HE 978-1-108-84360-7 Hardback £54.99 / US$69.99 P

Machine Learning

Mathematics for Machine Learning

Essentials of Pattern Recognition

Machine Learning Refined

Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong

An Accessible Approach Jianxin Wu

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

An accessible undergraduate introduction to the concepts and methods in pattern recognition, machine learning and deep learning.

Foundations, Algorithms, and Applications Second edition Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos

April 2020 253 x 177 mm 398pp 3 b/w illus. 106 colour illus.

PB, HB, EB, HE 978-1-108-45514-5 Paperback £39.99 / US$51.99 P

November 2020 244 x 170 mm 398pp HB, EB, HE 978-1-108-48346-9 Hardback £54.99 / US$80.99 X

An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises.

January 2020 247 x 174 mm 594pp 316 colour illus. 127 exercises HB, EB, HE 978-1-108-48072-7 Hardback £57.99 / US$78.99 X

High-Dimensional Data Analysis with Low-Dimensional Models Principles, Computation, and Applications John Wright, Yi Ma

Connects fundamental mathematical theory with real-world problems, through efficient and scalable optimization algorithms.

January 2022 244 x 170 mm 650pp HB, EB, HE 978-1-108-48973-7 Hardback £62.99 / US$83.99 P

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Machine Learning

Machine Learning Fundamentals A Concise Introduction Hui Jiang

Bayesian Reasoning and Machine Learning David Barber

A coherent introduction to core concepts and deep learning techniques that are critical to academic research and real-world applications.

A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.

November 2021 253 x 203 mm 418pp 203 colour illus.

PB, HB, EB, CO 978-1-108-94002-3 Paperback £42.99 / US$54.99 P

February 2012 246 x 189 mm 735pp 287 b/w illus. 1 table 260 exercises HB, EB, HE 978-0-521-51814-7 Hardback £66.99 / US$88.99 X

Machine Learning

Bandit Algorithms Tor Lattimore, Csaba Szepesvári

A comprehensive and rigorous introduction for graduate students and researchers, with applications in sequential decision-making problems.

July 2020 247 x 174 mm 536pp HB, EB, CO 978-1-108-48682-8 Hardback £41.99 / US$52.99 P

From Theory to Algorithms Shai Shalev-Shwartz, Shai Ben-David

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

July 2014 253 x 177 mm 410pp 47 b/w illus. 123 exercises HB, EB, CO 978-1-107-05713-5 Hardback £50.99 / US$66.99 X

AI, Natural Language Processing, Robotics, and Computer Vision

Machine Learning

Artificial Intelligence

The Art and Science of Algorithms that Make Sense of Data Peter Flach

Foundations of Computational Agents Third edition David L. Poole, Alan K. Mackworth

Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.

A comprehensive learning resource for undergraduate and graduate students, with new chapters on deep learning, causality, and social impact.

The first textbook on adversarial machine learning, including both attacks and defenses, background material, and hands-on student projects.

September 2012 246 x 189 mm 409pp 120 colour illus. 15 tables PB, HB, EB, CO 978-1-107-42222-3 Paperback £47.99 / US$62.99 P

July 2023 254 x 177 mm 900pp HB, EB, HE 978-1-00-925819-7 Hardback £57.99 / US$74.99 X

August 2023 244 x 170 mm 350pp HB, EB, HE 978-1-00-931567-8 Hardback £54.99 / US$69.99 X

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Understanding Machine Learning

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Adversarial Learning and Secure AI David J. Miller, Zhen Xiang, George Kesidis

Computational Principles of Mobile Robotics Third edition Gregory Dudek, Michael Jenkin

A graduate textbook providing a comprehensive introduction to mobile robotics, with hands-on examples in ROS 2.

February 2024 254 x 178 mm 453pp HB, EB, HE 978-1-108-73638-1 Paperback £49.99 / US$64.99 X

AI, Natural Language Processing, Robotics, and Computer Vision

Modern Robotics

Computer Vision

Virtual Reality

Hey Cyba

Mechanics, Planning, and Control Kevin M. Lynch, Frank C. Park

Models, Learning, and Inference Simon J. D. Prince

Steven M. LaValle

The Inner Workings of a Virtual Personal Assistant Steve Young

A modern and unified treatment of the mechanics, planning, and control of robots, suitable for a first course in robotics.

A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms.

May 2017 253 x 177 mm 544pp HB, EB 978-1-107-15630-2 Hardback £64.99 / US$82.99 X

August 2012 253 x 177 mm 598pp 357 colour illus. 5 tables 201 exercises HB, EB, HE 978-1-107-01179-3 Hardback £69.99 / US$94.99 X

An interdisciplinary text for students, researchers, and developers that blends foundations of virtual reality with industry insights.

October 2023 253 x 177 mm 392pp HB, EB, CO 978-1-107-19893-7 Hardback £59.99 / US$79.99 P

Reveals how AI works and provides insight into what we can expect of it now and in the future.

April 2021 228 x 152 mm 254pp PB, HB, EB, CO 978-1-108-97236-9 Paperback £18.99 / US$24.99 G

Data Science

AI, Natural Language Processing, Robotics, and Computer Vision

NEW

Natural Language Processing A Machine Learning Perspective Yue Zhang, Zhiyang Teng

This undergraduate textbook introduces essential machine learning concepts in NLP in a unified and gentle mathematical framework.

January 2021 246 x 189 mm 484pp HB, EB, HE 978-1-108-42021-1 Hardback £57.99 / US$78.99 P

Time Series for Data Scientists Data Management, Description, Modeling and Forecasting Juana Sanchez

A user-friendly, introductory, learningby-doing bridge between classical and machine learning time series analysis with R. May 2023 244 x 170 mm 550pp HB, EB, HE 978-1-108-83777-4 Hardback £59.99 / US$74.99 P

Introduction to Probability and Statistics for Data Science

A Hands-On Introduction to Data Science

with R Steven E. Rigdon, Ronald D. Fricker, Jr., Douglas C. Montgomery

An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.

For students in statistics, data science, engineering, and science programs needing a solid course in statistical theory and methods.

April 2020 246 x 189 mm 424pp 5 b/w illus. 135 colour illus.

36 tables 154 exercises HB, EB, HE 978-1-108-47244-9 Hardback £43.99 / US$57.99 P

December 2024 253 x 203 mm c. 840pp HB, EB, HE 978-1-107-11304-6 Hardback £67.99 / US$89.99 X

Chirag Shah

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Data Science

Large-Scale Data Analytics with Python and Spark A Hands-on Guide to Implementing Machine Learning Solutions Isaac Triguero, Mikel Galar

A hands-on textbook for courses on large-scale data analytics and designing machine learning solutions.

November 2023 244 x 170 mm 422pp PB, EB, HE 978-1-00-931825-9 Paperback £29.99 / US$39.99 X

Network Models for Data Science

Data-Driven Science and Engineering

Inference and Learning from Data

Theory, Algorithms, and Applications Alan Julian Izenman

Machine Learning, Dynamical Systems, and Control Second edition Steven L. Brunton, J. Nathan Kutz

Foundations Volume 1 Ali H. Sayed

This is the first book to describe modern methods for analyzing complex networks arising from a wide range of disciplines.

January 2023 254 x 177 mm 550pp HB, EB, HE 978-1-108-83576-3 Hardback £56.99 / US$74.99 X

A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

May 2022 253 x 177 mm 614pp HB, EB, HE 978-1-00-909848-9 Hardback £49.99 / US$64.99 P

Discover core topics in inference and learning with the first volume of this extraordinary three-volume set.

December 2022 244 x 170 mm 1010pp HB, EB, HE 978-1-00-921812-2 Hardback £84.99 / US$110.00 X

Data Science

Inference and Learning from Data

Inference and Learning from Data

Inference Volume 2 Ali H. Sayed

Learning Volume 3 Ali H. Sayed

Discover techniques for inferring unknown variables and quantities with the second volume of this extraordinary three-volume set.

Discover data-driven learning methods with the third volume of this extraordinary three-volume set.

December 2022 244 x 170 mm 1070pp HB, EB, HE 978-1-00-921826-9 Hardback £74.99 / US$105.00 X

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December 2022 244 x 170 mm 990pp HB, EB, HE 978-1-00-921828-3 Hardback £74.99 / US$105.00 X

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Computer Age Statistical Inference, Student Edition Algorithms, Evidence, and Data Science Bradley Efron, Trevor Hastie

Now in paperback and fortified with exercises, this brilliant, enjoyable text demystifies data science, statistics and machine learning.

Institute of Mathematical Statistics Monographs, 6 June 2021 228 x 152 mm 506pp PB, EB, HE 978-1-108-82341-8 Paperback £30.99 / US$41.99 P

Mining of Massive Datasets Third edition Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman

Now in its third edition, this book focuses on practical algorithms for mining data from even the largest datasets.

January 2020 244 x 170 mm 565pp 76 b/w illus. 250 exercises HB, EB, HE 978-1-108-47634-8 Hardback £64.99 / US$83.99 P

Data Science

Data Mining and Machine Learning Fundamental Concepts and Algorithms Second edition Mohammed J. Zaki, Wagner Meira, Jr

New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.

January 2020 253 x 177 mm 776pp 297 b/w illus.

HB, EB, HE 978-1-108-47398-9 Hardback £62.99 / US$83.99 P

Data Analysis for Business, Economics, and Policy Gábor Békés, Gábor Kézdi

A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data.

May 2021 246 x 189 mm 738pp PB, HB, EB, HE 978-1-108-71620-8 Paperback £49.99 / US$67.99 X

Optimization for Data Analysis

Foundations of Data Science

Stephen J. Wright, Benjamin Recht

Avrim Blum, John Hopcroft, Ravindran Kannan

A concise text that presents and analyzes the fundamental techniques and methods in optimization that are useful in data science.

Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.

April 2022 229 x 152 mm 238pp HB, EB, CO 978-1-316-51898-4 Hardback £37.99 / US$49.99 P

January 2020 253 x 177 mm 432pp HB, EB, CO 978-1-108-48506-7 Hardback £44.99 / US$57.99 X

Data Science

A First Course in Statistical Programming with R

Principles of Database Management

Third edition W. John Braun, Duncan J. Murdoch

The Practical Guide to Storing, Managing and Analyzing Big and Small Data Wilfried Lemahieu, Seppe vanden Broucke, Bart Baesens

Get started computing with data.

Learn general principles while learning R – now including the tidyverse.

May 2021 246 x 189 mm 280pp PB, EB, HE 978-1-108-99514-6 Paperback £36.99 / US$46.99 P

Introductory, theory-practice balanced text teaching the fundamentals of databases to advanced undergraduates or graduate students in information systems or computer science.

Computer Architecture and Organization

Introduction to Information Retrieval Christopher D. Manning Prabhakar Raghavan, Hinrich Schütze

A class-tested and up-to-date textbook for introductory courses on information retrieval.

September 2008 253 x 177 mm 506pp 5 b/w illus. 47 tables 263 exercises HB, EB, HE 978-0-521-86571-5 Hardback £54.99 / US$71.99 X

Computer Architecture for Scientists Principles and Performance Andrew A. Chien

A principled, high-level view of computer performance and how to exploit it. Ideal for software architects and data scientists.

March 2022 229 x 152 mm 264pp HB, EB, HE 978-1-316-51853-3 Hardback £49.99 / US$64.99 P

July 2018 246 x 189 mm 808pp 439 colour illus. 163 tables HB, EB, HE 978-1-107-18612-5 Hardback £59.99 / US$78.99 X

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Computer Architecture and Organization

Logic in Computer Science

Digital Design Using VHDL

Modelling and Reasoning about Systems Second edition Michael Huth, Mark Ryan

A Systems Approach William J. Dally, R. Curtis Harting, Tor M. Aamodt

Provides a sound basis in logic, and introduces logical frameworks used in modelling, specifying and verifying computer systems.

Provides students with a systemlevel perspective and the tools they need to analyze and design complete digital systems using VHDL.

August 2004 247 x 174 mm 440pp 10 tables 400 exercises PB, EB, HE 978-0-521-54310-1 Paperback £54.99 / US$72.99 X

December 2015 246 x 189 mm 721pp 489 b/w illus. 68 tables HB, EB, HE 978-1-107-09886-2 Hardback £62.99 / US$89.99 X

Digital Design A Systems Approach William James Dally, R. Curtis Harting

This book provides students with a system-level perspective and the tools they need to analyze and design complete digital systems using Verilog.

November 2012 246 x 189 mm 636pp 469 b/w illus. 68 tables 639 exercises HB 978-0-521-19950-6 Hardback £60.99 / US$82.99 X

Foundations and Algorithms

How to Think about Algorithms Second edition Jeff Edmonds

Exceptionally student-friendly, now with over 150 new exercises, key concept summaries, and a new chapter on machine learning algorithms.

February 2024 244 x 170 mm 500pp PB, HB, HE 978-1-00-930213-5 Paperback £29.99 / US$39.99 X

Foundations and Algorithms

Algorithms Illuminated Omnibus Edition Tim Roughgarden

Algorithms Illuminated teaches the basics and key techniques of algorithms in the most accessible way imaginable.

January 2023 253 x 177 mm 690pp 350 b/w illus. 60 tables 200 exercises HB 978-0-9992829-8-4 Hardback £47.99 / US$59.97 X Published by SoundLikeYourself Publishing

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Introduction to Probability for Computing Mor Harchol-Balter

A highly engaging and interactive undergraduate textbook specifically written for computer science courses.

September 2023 244 x 170 mm 555pp HB, EB, HE 978-1-00-930907-3 Hardback £54.99 / US$69.99 X

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Probability and Computing Randomization and Probabilistic Techniques in Algorithms and Data Analysis Second edition Michael Mitzenmacher, Eli Upfal

This greatly expanded new edition offers a comprehensive introduction to randomization and probabilistic techniques in modern computer science.

July 2017 253 x 177 mm 484pp 8 b/w illus. 1 table HB, EB 978-1-107-15488-9 Hardback £54.99 / US$93.99 X

The Probability Companion for Engineering and Computer Science Adam Prügel-Bennett

Using examples and building intuition, this friendly guide helps readers understand and use probabilistic tools from basic to sophisticated.

January 2020 253 x 177 mm 470pp 356 b/w illus.

PB, HB, EB, CO 978-1-108-72770-9 Paperback £46.99 / US$62.99 P

Foundations and Algorithms

Introduction to Proofs and Proof Strategies Shay Fuchs

With a conversational style and no prerequisites, this transition to advanced mathematics emphasizes creative thinking and problemsolving.

Cambridge Mathematical Textbooks June 2023 254 x 178 mm 349pp PB, EB, HE 978-1-00-909628-7 Paperback £34.99 / US$44.99 X

How to Prove It A Structured Approach Third edition Daniel J. Velleman

Helps students transition from problem solving to proving theorems, with a new chapter on number theory and over 150 new exercises.

July 2019 228 x 152 mm 468pp 47 b/w illus.

PB, HB, EB, HE 978-1-108-43953-4 Paperback £35.99 / US$41.99 X

Connecting Discrete Mathematics and Computer Science Second edition David Liben-Nowell

An approachable textbook connecting the mathematical foundations of computer science to broad-ranging and compelling applications throughout the field.

Complexity Science The Study of Emergence Henrik Jeldtoft Jensen

This introductory textbook provides detailed coverage of the rapidly growing field of complexity science, for a broad audience of readers.

November 2022 254 x 178 mm 458pp HB, EB, HE 978-1-108-83476-6 Hardback £39.99 / US$49.99 X

August 2022 253 x 203 mm 690pp HB, EB, HE 978-1-00-915049-1 Hardback £57.99 / US$74.99 X

Foundations and Algorithms

Computational Complexity

An Invitation to Combinatorics

Combinatorial Mathematics

Game Theory Basics

A Modern Approach Sanjeev Arora, Boaz Barak

Shahriar Shahriari

Douglas B. West

A conversational introduction to combinatorics for upper undergraduates, emphasizing problem solving and active student participation.

This is the most readable and thorough graduate textbook and reference for combinatorics, covering enumeration, graphs, sets, and methods.

A lively introduction to Game Theory, ideal for students in mathematics, computer science, or economics.

Cambridge Mathematical Textbooks July 2021 246 x 189 mm 628pp HB, EB, HE 978-1-108-47654-6 Hardback £38.99 / US$49.99 X

July 2020 246 x 189 mm 988pp 2200 exercises HB, EB, HE 978-1-107-05858-3 Hardback £65.99 / US$83.99 X

New and classical results in computational complexity, including interactive proofs, PCP, derandomization, and quantum computation. Ideal for graduate students.

June 2009 253 x 215 mm 594pp 73 b/w illus. 6 tables 307 exercises HB, EB, CO 978-0-521-42426-4 Hardback £55.00 / US$72.00 X

Bernhard von Stengel

August 2021 246 x 189 mm 374pp PB, HB, EB, HE 978-1-108-82423-1 Paperback £36.99 / US$46.99 X

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Linear Algebra

Foundations and Algorithms

Mathematical Logic through Python Yannai A. Gonczarowski, Noam Nisan

A unique approach to mathematical logic where students implement the underlying concepts and proofs in the Python programming language.

September 2022 253 x 177 mm 284pp PB, HB, EB, CO 978-1-108-94947-7 Paperback £22.99 / US$29.99 P

Matrix Mathematics A Second Course in Linear Algebra Second edition Stephan Ramon Garcia, Roger A. Horn

A modern matrix-based approach to a rigorous second course in linear algebra for mathematics, data science, and physical science majors.

Cambridge Mathematical Textbooks May 2023 254 x 178 mm 500pp HB, EB, HE 978-1-108-83710-1 Hardback £54.99 / US$69.99 X

Linear Algebra

A First Course in Network Science

Gilbert Strang

Filippo Menczer, Santo Fortunato, Clayton A. Davis

NOT FOR SALE IN NORTH AMERICA January 2019 234 x 191 mm 446pp HB 978-0-692-19638-0 Hardback £62.99 X

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Jeffrey A. Fessler, Raj Rao Nadakuditi

Master matrix methods via engaging data-driven applications, aided by classroom-tested quizzes, homework exercises and online Julia demos.

May 2024 244 x 170 mm 450pp HB, EB, HE 978-1-00-941814-0 Hardback £49.99 / US$64.99 X

Introduction to Applied Linear Algebra Vectors, Matrices, and Least Squares Stephen Boyd, Lieven Vandenberghe

A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.

June 2018 246 x 189 mm 474pp HB, EB, HE 978-1-316-51896-0 Hardback £40.99 / US$51.99 X

Network Science

Linear Algebra and Learning from Data From Gilbert Strang, the first textbook that teaches linear algebra together with deep learning and neural nets.

Linear Algebra for Data Science, Machine Learning, and Signal Processing

A practical introduction to network science for students across business, cognitive science, neuroscience, sociology, biology, engineering and other disciplines.

February 2020 246 x 189 mm 300pp 131 b/w illus. 131 colour illus.

HB, EB, HE 978-1-108-47113-8 Hardback £38.99 / US$51.99 X

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Network Science Albert-László Barabási, Márton Pósfai

Illustrated throughout in full colour, this pioneering text is the only book you need for an introduction to network science.

July 2016 246 x 189 mm 475pp 371 colour illus. 12 tables 30 exercises HB 978-1-107-07626-6 Hardback £46.99 / US$61.99 P

Networks, Crowds, and Markets Reasoning about a Highly Connected World David Easley, Jon Kleinberg

Reveals the interdisciplinary field of networks, which changes how we look at social, financial and technological interactions in modern society.

September 2010 253 x 215 mm 744pp 332 b/w illus. 128 exercises HB, EB, CO 978-0-521-19533-1 Hardback £54.99 / US$71.99 X

Programming and Software Development

Programming Languages

Software Engineering

Essentials of Software Testing

Introduction to Software Testing

Build, Prove, and Compare Norman Ramsey

Basic Principles and Best Practices Ravi Sethi

Ralf Bierig, Stephen Brown, Edgar Galván, Joe Timoney

Second edition Paul Ammann, Jeff Offutt

Designed for introductory courses with a significant team project, this textbook presents concepts with reallife case studies and examples.

This accessible introduction demonstrates a range of testing techniques in the context of a single worked example that runs throughout.

This classroom-tested new edition features expanded coverage of the basics and test automation frameworks, with new exercises and examples.

December 2022 244 x 170 mm 360pp HB, EB, HE 978-1-316-51194-7 Hardback £44.99 / US$59.99 X

August 2021 244 x 170 mm 318pp HB, EB, HE 978-1-108-83334-9 Hardback £46.99 / US$62.99 P

December 2016 253 x 177 mm 364pp 79 b/w illus.

HB, EB, HE 978-1-107-17201-2 Hardback £54.99 / US$71.99 X

Teaches students about great programming-language ideas and how to use them in programming practice.

October 2022 254 x 178 mm 600pp HB, EB, HE 978-1-107-18018-5 Hardback £64.99 / US$84.99 P

Programming and Software Development

Competitive Programming in Python 128 Algorithms to Develop your Coding Skills Christoph Dürr, Jill-Jênn Vie, Greg Gibbons, Danièle Gibbons

All the algorithms, proofs, and implementations in Python you need to know for tech job interviews and coding competitions.

December 2020 244 x 170 mm 264pp PB, EB, CO 978-1-108-71682-6 Paperback £33.99 / US$43.99 P

Programming in Haskell Second edition Graham Hutton

This extensively updated and expanded version of the best-selling first edition now covers recent and more advanced features of Haskell.

September 2016 216 x 138 mm 318pp 1 b/w illus. 120 exercises PB, EB, HE 978-1-316-62622-1 Paperback £34.99 / US$46.99 X

Modern Compiler Implementation in Java

Modern Compiler Implementation in ML

Second edition Andrew W. Appel, Jens Palsberg

Andrew W. Appel

The second edition features a redesigned compiler project in Java, for a subset of Java itself.

October 2002 246 x 156 mm 512pp 80 b/w illus. 35 tables 135 exercises HB, EB, CO 978-0-521-82060-8 Hardback £67.99 / US$104.00 X

Describes all phases of a modern compiler, including techniques in code generation and register allocation for imperative, functional and object-oriented languages.

July 2004 246 x 189 mm 552pp 80 b/w illus. 34 tables 117 exercises PB, EB, CO 978-0-521-60764-3 Paperback £62.99 / US$92.99

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Quantum Computing

Programming and Software Development

Modern Compiler Implementation in C Andrew W. Appel, Maia Ginsburg

Describes all phases of a modern compiler, including techniques in code generation and register allocation for imperative, functional and object-oriented languages.

July 2004 246 x 189 mm 556pp 80 b/w illus. 34 tables 117 exercises PB, EB, CO 978-0-521-60765-0 Paperback £64.99 / US$92.99

Quantum Computation and Quantum Information 10th Anniversary Edition Michael A. Nielsen, Isaac L. Chuang

This 10th anniversary edition includes an introduction from the authors setting the work in context.

December 2010 247 x 174 mm 702pp 200 b/w illus. 10 tables 598 exercises HB, EB, HE 978-1-107-00217-3 Hardback £59.99 / US$83.99 X

Quantum Computing

Quantum Computer Science An Introduction N. David Mermin

A concise introduction to quantum computation for computer scientists who know nothing about quantum theory.

August 2007 246 x 189 mm 233pp 67 b/w illus.

HB, EB, CO 978-0-521-87658-2 Hardback £54.99 / US$74.99 X

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Quantum Computing since Democritus Scott Aaronson

Takes students and researchers on a tour through some of the deepest ideas of maths, computer science and physics.

March 2013 228 x 152 mm 398pp 25 b/w illus.

PB, EB, CO 978-0-521-19956-8 Paperback £43.99 / US$57.99 P

Quantum Computing for Computer Scientists Noson S. Yanofsky, Mirco A. Mannucci

Finally, a textbook that explains quantum computing using techniques and concepts familiar to computer scientists.

November 2008 253 x 177 mm 402pp 4 b/w illus. 245 exercises HB, EB, CO 978-0-521-87996-5 Hardback £69.99 / US$94.99 X

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Point lecture slides

nd PPT figures from the book

n: © Elin Zhou.

ndrew Ward

• Appendices review the underlying mathematics, and provide the necessary mapping to open-source ML packages.

Lecture slides

Website with interactive AI system Programming assignments Further reading

URLs for further reading

“The chapters on cloud computing and responsible AI cover two topics particularly relevant to today’s machine learning practices, yet rarely found at such depth and quality in other machine learning books. A self-contained and much-needed book that is highly accessible to readers of diverse backgrounds.”

Dr Haiping Lu, University of Sheffield “… an accessible yet far-reaching treatment of practical machine learning. Professor Shah leverages his years of experience creating, teaching, and applying machine learning, in academia as well as industry, to present material that ranges from classical topics to current trends.”

Dr. Rishabh Mehrotra, Director, Machine Learning at ShareChat

shah-ML For instructors: Example syllabi Solutions manual Instructor's manual Lecture slides

o / iStock/ Getty Images Plus. aylor

Cover design by Holly Johnson

Cover design: Andrew Ward

“It is an exceptional book for software engineering because it teaches how to work with real customers using today’s development processes. This book, more than others I’ve seen, prepares students for their work environment after graduation.”

JOANN J. ORDILLE Rutgers University “The focus on practice, with detailed examples on how to set up and run semester-long course projects, was extremely useful. This textbook covers exactly what is needed for the introduction to software engineering.”

AUDRIS MOCKUS University of Tennessee “This book will be invaluable for students, researchers, and especially for working programmers. It combines the hard-won wisdom of a senior software executive, the experiences of a seasoned programmer, real-life stories as a member and leader of software teams, and the skills of a superb writer and excellent teacher.”

JON BENTLEY The United States Military Academy West Point

“Dr. Shah has written a fabulous introduction to data science for a broad audience. His book offers many learning opportunities, including explanations of core principles, thoughtprovoking conceptual questions, and hands-on examples and exercises. It will help readers gain proficiency in this important area and quickly start deriving insights from data.”

Ryen W. White, Microsoft Research AI

CHIRAG SHAH

Software engineering is as much about teamwork as it is about technology. This introductory textbook covers both. For courses featuring a team project, it offers tips and templates for aligning classroom concepts with the needs of the students' projects.

Covers the why's and how-to's of agile development, requirements discovery, modular system design, and effective test selection.

Illustrates the practical application of the concepts with real-life case studies and examples.

Introduces metrics, measurement, data display and description, with applications for improving the qualities of products, processes, and operations.

sethi Instructor resources PowerPoint slides and JPEGs of all the figures and tables from the book Lecture slides Student resources Report templates

Cover illustration: Busy Bees. Bees are among the natural world's engineers. The two bees represent teamwork. The honeycomb illustrates architecture. The honey that they produce represents the benefits from their projects. Image credit: Brian Hagiwara / The Image Bank / Getty Images.

Instructor’s manual Solutions

Cover image: Donald Iain Smith, via Getty Images. Cover design by Zoe Naylor.

Cover image courtesy of Daniel Bosma / Moment / Getty Images

Cover image: © SCIEPRO / Science Photo Library / Getty Images

“I loved this book and highly recommend it to all software engineering students. There isn’t another text on the market that captures the main essence of the field so successfully.”

HERBERT H. TSANG Simon Fraser University

Theresa Dirndorfer Anderson, Master of Data Science & Innovation (MDSI), University of Technology Sydney

shah Datasets Programs

Downloadable code

9781316511947 SETHI – SOFT WARE ENGINEERING PPC C M Y K

Professor Akhilesh Bajaj, The University of Tulsa

THIRD EDITION

FOUNDATIONS OF COMPUTATIONAL AGENTS

Software Engineering

“Written by a great teacher who truly understands the material, the book is conversational and very approachable.”

For students

ARTIFICIAL INTELLIGENCE

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book’s web site.

SETHI

Madhu Kurup, Vice President, Indeed.com

Sriram Natarajan, University of Texas at Dallas

Solutions manual

A HANDS-ON INTRODUCTION TO MACHINE LEARNING

“… an approachable exposition of machine learning, with theories and context based on real-life, practical applications.”

“... a must-have on the bookshelf for AI instructors, students, researchers, and practitioners.”

Online Resources www.cambridge.org/poole3e

For instructors

SHAH

-world examples, and practical activities, designed to teach g in a way that is easy nd apply. It assumes wledge of technology, al resource for students s, including those who puter science. All the are covered, including nsupervised learning, reinforcement learning, ices, and a chapter on the posing problems within ile Python is used as the e, many exercises will also ns provided in R for greater e of online resources is ort teaching across a range ses, including example ns manual, and lecture and code are also available nts, giving them everything ctice the examples and book.

-on examples

• Five larger case studies are developed throughout the book, and connect the design approaches to the applications.

Carla Gomes, Cornell University

“This book provides a beautiful exposition of the mathematics underpinning modern machine learning. Highly recommended for anyone wanting a one-stop shop to acquire a deep understanding of machine learning foundations.”

– Pieter Abbeel, University of California, Berkeley

MATHEMATICS FOR MACHINE LEARNING

THIRD EDITION

Software Engineering Basic Principles and Best Practices

RAVI SETHI

A HANDS-ON INTRODUCTION TO DATA SCIENCE

ructors

ons manual

• Every algorithm is presented in pseudocode and in open-source AIPython code, enabling students to experiment with and build on the implementations.

“... an amazing introduction to the field of AI ... seamlessly integrates the exciting developments in deep learning into the broader AI context [and] highlights the societal impact of AI.”

“Chirag’s extensive experience as a teacher shines through in this textbook, which lives up to its promise to be a ‘hands-on’ introduction to data science. Students have a chance to apply their learning to real-life examples from diverse fields, with hands-on examples that build on basic techniques and utilize tools of data science practice throughout the book. I am particularly pleased to see him weave human issues into his approach, putting principles ahead of particular tools, and pointing to ethical challenges at various stages of working with data to help his audience develop an appreciation of ways in which context and interpretation shape data practices. He exposes students to a more nuanced perspective in which human as well as machine input shapes data science outcomes. It is an awareness that we all will need if we are to use data appropriately to tackle the complex challenges we face today.”

SHAH

nline Resources ww.cambridge.org/harchol-balter

Rina Dechter, University of California, Irvine, author of Constraint Programming

Mor Harchol-Balter

• Agent designs progress gradually from the simple to the complex.

“The field of machine learning has grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This comprehensive text covers the key mathematical concepts that underpin modern machine learning, with a focus on linear algebra, calculus, and probability theory. It will prove valuable both as a tutorial for newcomers to the field and as a reference text for machine learning researchers and engineers.”

– Christopher Bishop, Microsoft Research Cambridge, UK

This book introduces the field of data science in a practical and accessible manner, using a handson approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to develop a firm understanding of the subject easily without a strong technical background, as well as presenting them with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams, and curriculum suggestions. This entrylevel textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.

MATHEMATICS FOR MACHINE LEARNING

Manuel Blum, University of California, Berkeley, and Carnegie Mellon University

full-color illustrations and almost cises.

“... covers everything you want to know about AI in a very accessible style, accompanied by a wide range of thoughtful and challenging exercises.”

DAVID L. POOLE AND ALAN K. MACKWORTH

“This book provides great coverage of all the basic mathematical concepts for machine learning. I’m looking forward to sharing it with students, colleagues, and anyone interested in building a solid understanding of the fundamentals.”

– Joelle Pineau, McGill University and Facebook

DEIS ENR OTH, FAIS AL, and ONG

“Mor is a great thinker, lecturer, and writer ...

I would love to have learned from this book as a student.”

• Each chapter now has a social impact section, enabling students to understand the impact of the various techniques as they learn.

Sheila McIlraith, University of Toronto

9781108472449 Shah PPC C M Y K

R. Srikant, University of Illinois at Urbana‑Champaign

• Every concept or algorithm is illustrated with a motivating concrete example.

“This book has it all. From core concepts, to algorithms and code ... gives the reader everything they need to build modern AI systems ... an outstanding resource for student and instructor alike.”

ARTIFICIAL INTELLIGENCE

• The novel agent design space, which provides a coherent framework for teaching and learning, making both easier.

“... a beautiful introduction to the topic for undergraduate students.”

Deisenrith et al. 9781108470049 PPC. C M Y K

Avrim Blum, Toyota Technological Institute at Chicago

Anna Karlin, University of Washington

Judea Pearl, UCLA, Turing Award winner and author of Causality and The Book of Why

Probability for Computing

Students and instructors will benefit from these features:

“... does a beautiful job of introducing students to probability! The book is full of great computer science-relevant examples, wonderful intuition, simple and clear explanations, and mathematical rigor.”

“... should become the standard text of AI education ... Highly recommended.”

Introduction to

“... a fantastic introduction to probability for computer scientists ... Highly recommended!”

9781009258197: Poole and Mackworth: PPC: C M Y K

n a sequence of questions and to engage students and encourage tively to think about and better and definitions, equations, and ental concepts.

A comprehensive textbook for undergraduate and graduate AI courses, explaining modern artificial intelligence and its social impact, and integrating theory and practice. This extensively revised new edition now includes chapters on deep learning, including generative AI, the social impacts of AI, and causality.

POOLE AND MACKWORTH

es students with numerous real-world er science applications, such as hash sign, capacity provisioning in data web page ranking, disk modeling, pagation, deducing signals in vironments, error-correcting codes, primality testing, etc.

Eytan Modiano, Massachusetts Institute of Technology

Introduction to Probability for Computing

es

“... highly engaging and also strongly motivated by real-world computing applications ... approachable and fun for undergraduate students.”

Harchol-Balter

science students can find probability g and remote from their computing Maximize student engagement and ding with this uniquely rigorous yet undergraduate text, which is written for computing students. It combines y basics with a wide range of g-relevant topics, including statistical computer system simulation, d algorithms, and Markov modeling g systems. The book has been classd will be an invaluable learning tool our course covers probability with with simulation, with randomized , or with stochastic processes.

A HANDS-ON INTRODUCTION TO DATA

SCIENCE

CHIRAG SHAH

Marc Peter Deisenroth A. Aldo Faisal Cheng Soon Ong

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