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Applications of cryptography are plenty in everyday life. This guidebook is about the security analysis or 'cryptanalysis' of the basic building blocks on which these applications rely. Rather than covering a variety of techniques at an introductory level, this book...
This introduction to quantum computing from a classical programmer's perspective is meant for students and practitioners alike. More than 50 quantum techniques and algorithms are explained with mathematical derivations and code for simulation, using an open-source code...
Bridge the gap between theoretical concepts and their practical applications with this rigorous introduction to the mathematics underpinning data science. It covers essential topics in linear algebra, calculus and optimization, and probability and statistics,...
Bridging theory and practice in network data analysis, this guide offers an intuitive approach to understanding and analyzing complex networks. It covers foundational concepts, practical tools, and real-world applications using Python frameworks including NumPy, SciPy,...
Teaching fundamental design concepts and the challenges of emerging technology, this textbook prepares students for a career designing the computer systems of the future. Self-contained yet concise, the material can be taught in a single semester, making it perfect for...
Tensors are essential in modern day computational and data sciences. This book explores the foundations of tensor decompositions, a data analysis methodology that is ubiquitous in machine learning, signal processing, chemometrics, neuroscience, quantum computing,...
The 1994 discovery of Shor's quantum algorithm for integer factorization—an important practical problem in the area of cryptography—demonstrated quantum computing's potential for real-world impact. Since then, researchers have worked intensively to expand the list of...
This book introduces relevant and established data-driven modeling tools currently in use or in development, which will help readers master the art and science of constructing models from data and dive into different application areas. It presents statistical tools...
Harnessing the power of data and AI methods to tackle complex societal challenges requires transdisciplinary collaborations across academia, industry, and government. In this compelling book, Munther A. Dahleh, founder of the MIT Institute for Data, Systems, and Society...
The use of differential equations on graphs as a framework for the mathematical analysis of images emerged about fifteen years ago and since then it has burgeoned, and with applications also to machine learning.The authors have written a bird's eye view of theoretical...
Providing an in-depth treatment of an exciting research area, this text's central topics are initial algebras and terminal coalgebras, primary objects of study in all areas of theoretical computer science connected to semantics. It contains a thorough presentation of...
This guide illuminates the intricate relationship between data management, computer architecture, and system software. It traces the evolution of computing to today's data-centric focus and underscores the importance of hardware-software co-design in achieving efficient...
The latest version of 'Programming in Ada' covers the full details of the core language Ada 2022 as approved by ISO in 2023, including new features that aid program proof and the efficient use of multicore architectures.The book is arranged in four parts.The first part...
As machine learning applications gain widespread adoption and integration in a variety of applications, including safety and mission-critical systems, the need for robust evaluation methods grows more urgent. This book compiles scattered information on the topic from...
Web3 is a new frontier of internet architecture emphasizing decentralization and user control. This text for MBA students and industry professionals explores keyWeb3 concepts, starting from foundational principles and moving to advanced topics like blockchain, smart...
Learn with confidence with this hands-on undergraduate textbook for CS2 courses. Active-learning and real-world projects underpin each chapter, briefly reviewing programming fundamentals then progressing to core data structures and algorithms topics including recursion,...
This book is designed to provide in-depth knowledge on how search plays a fundamental role in problem solving. Meant for undergraduate and graduate students pursuing courses in computer science and artificial intelligence, it covers a wide spectrum of search methods....
The role of robots in society keeps expanding and diversifying, bringing with it a host of issues surrounding the relationship between robots and humans. This introduction to human–robot interaction (HRI) by leading researchers in this developing field is the first to...
Using diverse real-world examples, this text examines what models used for data analysis mean in a specific research context. What assumptions underlie analyses, and how can you check them? Building on the successful 'Data Analysis and GraphicsUsing R,' 3rd edition...
Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package. Students are introduced to matrices in two preliminary chapters, before progressing to advanced topics such as the nuclear norm, proximal...
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