Higher Education
Computer Science 2024 Catalogue
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Contents Machine Learning
4
AI, Natural Language Processing, Robotics, and Computer Vision Data Science
Computer Architecture and Organization 8
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
Network Science
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Programming and Software Development
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Quantum Computing
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Key to formats
Artificial Intelligence
11
5 6
Foundations and Algorithms
Linear Algebra
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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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Machine Learning
NEW
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 £37.99 / US$49.99 P
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November 2020 244 x 170 mm 398pp HB, EB, HE 978-1-108-48346-9 Hardback £51.99 / US$76.99 X
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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 £54.99 / US$74.99 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 £59.99 / US$79.99 P
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 £39.99 / US$49.99 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 £63.99 / US$84.99 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
Understanding Machine Learning 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
NEW 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
Adversarial Learning and Secure AI
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 £45.99 / US$59.99 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
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
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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 £63.99 / US$79.99 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 £17.99 / US$23.99 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 £54.99 / US$74.99 P
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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
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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 £41.99 / US$54.99 P
c. August 2024 253 x 203 mm c. 840pp HB, EB, HE 978-1-107-11304-6 Hardback c.£64.99 / US$89.99 X
Chirag Shah
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
December 2022 244 x 170 mm 990pp HB, EB, HE 978-1-00-921828-3 Hardback £74.99 / US$105.00 X
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 £29.99 / US$39.99 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 £62.99 / US$79.99 P
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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 £59.99 / US$79.99 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$64.99 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 £42.99 / US$54.99 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 £34.99 / US$44.99 P
Introductory, theory-practice balanced text teaching the fundamentals of databases to advanced undergraduates or graduate students in information systems or computer science. July 2018 246 x 189 mm 808pp 439 colour illus. 163 tables HB, EB, HE 978-1-107-18612-5 Hardback £54.99 / US$74.99 X
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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 £53.99 / US$69.99 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
Computer Architecture and Organization
Foundations and Algorithms
NEW
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 £53.99 / US$69.99 X
December 2015 246 x 189 mm 721pp 489 b/w illus. 68 tables HB, EB, HE 978-1-107-09886-2 Hardback £59.99 / US$85.99 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 £57.99 / US$78.99 X
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
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
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 £49.99 / US$69.99 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 £44.99 / US$58.99 P
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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 £33.99 / US$39.99 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 £36.99 / US$47.99 X
July 2020 246 x 189 mm 988pp 2200 exercises HB, EB, HE 978-1-107-05858-3 Hardback £62.99 / US$79.99 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
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Bernhard von Stengel
August 2021 246 x 189 mm 374pp PB, HB, EB, HE 978-1-108-82423-1 Paperback £34.99 / US$44.99 X
Linear Algebra
Foundations and Algorithms
NEW
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
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. c. May 2024 244 x 170 mm c. 445pp 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 £38.99 / US$49.99 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 £36.99 / US$49.99 X
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 £44.99 / US$59.99 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
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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 £44.99 / US$59.99 P
December 2016 253 x 177 mm 364pp 79 b/w illus. HB, EB, HE 978-1-107-17201-2 Hardback £53.99 / US$69.99 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 £31.99 / US$41.99 P
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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 £33.99 / US$44.99 X
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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 £64.99 / 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 £59.99 / US$92.99
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 £63.99 / 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 £57.99 / US$79.99 X
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 £41.99 / US$54.99 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 £68.99 / US$94.99 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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develop your courseware using Cambridge content, your own learning resources or both design your course to meet your goals and fit your student and institutional needs. Learn more about how we can partner with you by visiting cup.org/courseware_solutions
For more information about creating custom courseware with Cambridge, or to discuss your course goals and ideas, get in touch: cup.org/courseware_partner
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Dr. Priya Jamkhedkar, Portland State University
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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
“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 “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
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.
“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
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
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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