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Explainable and Interpretable Reinforcement Learning for Robotics (Synthesis Lectures on Artificial Intelligence and Machine Learning)
by Dinesh Manocha Aaron M. Roth Ram D. Sriram Elham TabassiThis book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics. While RL in general has grown in popularity and been applied to increasingly complex problems, several challenges have impeded the real-world adoption of RL algorithms for robotics and related areas. These include difficulties in preventing safety constraints from being violated and the issues faced by systems operators who desire explainable policies and actions. Robotics applications present a unique set of considerations and result in a number of opportunities related to their physical, real-world sensory input and interactions. The authors consider classification techniques used in past surveys and papers and attempt to unify terminology across the field. The book provides an in-depth exploration of 12 attributes that can be used to classify explainable/interpretable techniques. These include whether the RL method is model-agnostic or model-specific, self-explainable or post-hoc, as well as additional analysis of the attributes of scope, when-produced, format, knowledge limits, explanation accuracy, audience, predictability, legibility, readability, and reactivity. The book is organized around a discussion of these methods broken down into 42 categories and subcategories, where each category can be classified according to some of the attributes. The authors close by identifying gaps in the current research and highlighting areas for future investigation.
Explainable and Responsible Artificial Intelligence in Healthcare
by Rishabha Malviya Sonali SundramThis book presents the fundamentals of explainable artificial intelligence (XAI) and responsible artificial intelligence (RAI), discussing their potential to enhance diagnosis, treatment, and patient outcomes. This book explores the transformative potential of explainable artificial intelligence (XAI) and responsible AI (RAI) in healthcare. It provides a roadmap for navigating the complexities of healthcare-based AI while prioritizing patient safety and well-being. The content is structured to highlight topics on smart health systems, neuroscience, diagnostic imaging, and telehealth. The book emphasizes personalized treatment and improved patient outcomes in various medical fields. In addition, this book discusses osteoporosis risk, neurological treatment, and bone metastases. Each chapter provides a distinct viewpoint on how XAI and RAI approaches can help healthcare practitioners increase diagnosis accuracy, optimize treatment plans, and improve patient outcomes. Readers will find the book: explains recent XAI and RAI breakthroughs in the healthcare system; discusses essential architecture with computational advances ranging from medical imaging to disease diagnosis; covers the latest developments and applications of XAI and RAI-based disease management applications; demonstrates how XAI and RAI can be utilized in healthcare and what problems the technology faces in the future. Audience The main audience for this book is targeted to scientists, healthcare professionals, biomedical industries, hospital management, engineers, and IT professionals interested in using AI to improve human health.
Explainable and Transparent AI and Multi-Agent Systems: 4th International Workshop, EXTRAAMAS 2022, Virtual Event, May 9–10, 2022, Revised Selected Papers (Lecture Notes in Computer Science #13283)
by Davide Calvaresi Amro Najjar Kary Främling Michael WinikoffThis book constitutes the refereed proceedings of the 4th International Workshop on Explainable and Transparent AI and Multi-Agent Systems, EXTRAAMAS 2022, held virtually during May 9–10, 2022. The 14 full papers included in this book were carefully reviewed and selected from 25 submissions. They were organized in topical sections as follows: explainable machine learning; explainable neuro-symbolic AI; explainable agents; XAI measures and metrics; and AI & law.
Explainable and Transparent AI and Multi-Agent Systems: 5th International Workshop, EXTRAAMAS 2023, London, UK, May 29, 2023, Revised Selected Papers (Lecture Notes in Computer Science #14127)
by Andrea Omicini Davide Calvaresi Amro Najjar Kary Främling Reyhan Aydogan Rachele Carli Giovanni Ciatto Yazan MuallaThis volume LNCS 14127 constitutes the refereed proceedings of the 5th International Workshop, EXTRAAMAS 2023, held in London, UK, in May 2023. The 15 full papers presented together with 1 short paper were carefully reviewed and selected from 26 submissions. The workshop focuses on Explainable Agents and multi-agent systems; Explainable Machine Learning; and Cross-domain applied XAI.
Explainable and Transparent AI and Multi-Agent Systems: 6th International Workshop, EXTRAAMAS 2024, Auckland, New Zealand, May 6–10, 2024, Revised Selected Papers (Lecture Notes in Computer Science #14847)
by Andrea Omicini Davide Calvaresi Amro Najjar Kary Främling Reyhan Aydogan Rachele Carli Giovanni Ciatto Joris HulstijnThis volume constitutes the papers of several workshops which were held in conjunction with the 6th International Workshop on Explainable and Transparent AI and Multi-Agent Systems, EXTRAAMAS 2024, in Auckland, New Zealand, during May 6–10, 2024. The 13 full papers presented in this book were carefully reviewed and selected from 25 submissions. The papers are organized in the following topical sections: User-centric XAI; XAI and Reinforcement Learning; Neuro-symbolic AI and Explainable Machine Learning; and XAI & Ethics.
Explainable and Transparent AI and Multi-Agent Systems: Third International Workshop, EXTRAAMAS 2021, Virtual Event, May 3–7, 2021, Revised Selected Papers (Lecture Notes in Computer Science #12688)
by Davide Calvaresi Amro Najjar Kary Främling Michael WinikoffThis book constitutes the proceedings of the Third International Workshop on Explainable, Transparent AI and Multi-Agent Systems, EXTRAAMAS 2021, which was held virtually due to the COVID-19 pandemic.The 19 long revised papers and 1 short contribution were carefully selected from 32 submissions. The papers are organized in the following topical sections: XAI & machine learning; XAI vision, understanding, deployment and evaluation; XAI applications; XAI logic and argumentation; decentralized and heterogeneous XAI.
Explainable, Transparent Autonomous Agents and Multi-Agent Systems: First International Workshop, EXTRAAMAS 2019, Montreal, QC, Canada, May 13–14, 2019, Revised Selected Papers (Lecture Notes in Computer Science #11763)
by Michael Schumacher Davide Calvaresi Amro Najjar Kary FrämlingThis book constitutes the proceedings of the First International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems, EXTRAAMAS 2019, held in Montreal, Canada, in May 2019. The 12 revised and extended papers presented were carefully selected from 23 submissions. They are organized in topical sections on explanation and transparency; explainable robots; opening the black box; explainable agent simulations; planning and argumentation; explainable AI and cognitive science.
Explainable, Transparent Autonomous Agents and Multi-Agent Systems: Second International Workshop, EXTRAAMAS 2020, Auckland, New Zealand, May 9–13, 2020, Revised Selected Papers (Lecture Notes in Computer Science #12175)
by Davide Calvaresi Amro Najjar Kary Främling Michael WinikoffThis book constitutes the proceedings of the Second International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems, EXTRAAMAS 2020, which was due to be held in Auckland, New Zealand, in May 2020. The conference was held virtually due to the COVID-19 pandemic.The 8 revised and extended papers were carefully selected from 20 submissions and are presented here with one demo paper. The papers are organized in the following topical sections: explainable agents; cross disciplinary XAI; explainable machine learning; demos.
Explaining Algorithms Using Metaphors
by Michal Forišek Monika SteinováThere is a significant difference between designing a new algorithm, proving its correctness, and teaching it to an audience. When teaching algorithms, the teacher's main goal should be to convey the underlying ideas and to help the students form correct mental models related to the algorithm. This process can often be facilitated by using suitable metaphors. This work provides a set of novel metaphors identified and developed as suitable tools for teaching many of the "classic textbook" algorithms taught in undergraduate courses worldwide. Each chapter provides exercises and didactic notes for teachers based on the authors' experiences when using the metaphor in a classroom setting.
Explanatory Animations in the Classroom: Student-Authored Animations as Digital Pedagogy (SpringerBriefs in Education)
by Brendan JacobsThis book provides groundbreaking evidence demonstrating how student-authored explanatory animations can embody and document learning as an exciting new development within digital pedagogy. Explanatory animations can be an excellent resource for teaching and learning but there has been an underlying assumption that students are predominately viewers rather than animation authors. The methodology detailed in this book reverses this scenario by putting students in the driver’s seat of their own learning. This signals not just a change in perspective, but a complete change in activity that, to continue the analogy, will forever change the conversation and make redundant phrases like “Are we there yet?” and “How much longer?” The digital nature of such practices provides compelling evidence for reconceptualising explanatory animation creation as a pedagogical activity that generates multimodal assessment data. Tying together related themes to advance approaches to evidence-based assessment using digital technologies, this book is intended for educators at any stage of their journey, including pre-service teachers.
Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models (Chapman & Hall/CRC Data Science Series)
by Tomasz Burzykowski Przemyslaw BiecekExplanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model's performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.
Exploding Data: Reclaiming Our Cyber Security in the Digital Age
by Michael ChertoffA former Secretary of Homeland Security examines our outdated laws regarding the protection of personal information, and the pressing need for change. Nothing undermines our freedom more than losing control of information about ourselves. And yet, as daily events underscore, we are ever more vulnerable to cyber-attack. In this bracing book, Michael Chertoff makes clear that our laws and policies surrounding the protection of personal information, written for an earlier time, are long overdue for a complete overhaul. On the one hand, the collection of data—more widespread by business than by government, and impossible to stop—should be facilitated as an ultimate protection for society. On the other, standards under which information can be inspected, analyzed, or used must be significantly tightened. In offering his compelling call for action, Chertoff argues that what is at stake is not so much the simple loss of privacy, which is almost impossible to protect, but of individual autonomy—the ability to make personal choices free of manipulation or coercion. Offering vivid stories over many decades that illuminate the three periods of data gathering we have experienced, Chertoff explains the complex legalities surrounding issues of data collection and dissemination today, and charts a path that balances the needs of government, business, and individuals alike. &“Surveys the brave new world of data collection and analysis…The world of data as illuminated here would have scared George Orwell.&”―Kirkus Reviews &“Chertoff has a unique perspective on data security and its implications for citizen rights as he looks at the history of and changes in privacy laws since the founding of the U.S.&”—Booklist
Exploding the Myths Surrounding ISO9000
by Andrew W. NicholsThe secrets of successful ISO9000 implementation Thousands of companies worldwide are reaping the benefits from adopting the ISO9000 family of quality management standards. However, there are many conflicting opinions about ISO9000s best practice approach. Some companies have delayed adopting ISO9000, or have chosen not to undertake implementation at all. This might be because of a lack of time and resources to investigate it properly, or because of misunderstandings about the way it works. So, how do we know who and what to believe? In Exploding the Myths Surrounding ISO9000, Andrew W Nichols debunks many of the common misconceptions about ISO9001 and describes the many advantages it brings. Drawing on more than 25 years of hands-on experience, Andy gives clear, practical and up-to-date advice on how to implement a Quality Management System to maximum effect. Full of real-life examples, this book enables you to read and interpret the ISO9000 documentation. With the advice in this book, you can foster an effective ISO9000 system that brings increased efficiencies, reduces waste and fuels growth in sales as you better understand and meet the needs of your customers. About the author Andrew W Nichols has more than 25 years of experience of management systems, in both the UK and the USA. As a trainer, he has delivered hundreds of ISO9000 related courses to audiences ranging from shop-floor personnel to CEOs of Fortune 500 companies. He has also led and contributed to the development of best in class training courses for a number of international standards. Andy is a regular contributor to the well-known Elsmar Cove internet forum for management systems. Read this unique book and make ISO9000 work for you.
Exploration of Novel Intelligent Optimization Algorithms: 12th International Symposium, ISICA 2021, Guangzhou, China, November 20–21, 2021, Revised Selected Papers (Communications in Computer and Information Science #1590)
by Yong Liu Kangshun Li Wenxiang WangThis book constitutes the refereed proceedings of the 12th International Symposium, ISICA 2021, held in Guangzhou, China, during November 19–21, 2021. The 48 full papers included in this book were carefully reviewed and selected from 99 submissions. They were organized in topical sections as follows: new frontier of multi-objective evolutionary algorithms; intelligent multi-media; data modeling and application of artificial intelligence; exploration of novel intelligent optimization algorithm; and intelligent application of industrial production.
Exploration of Quantum Transport Phenomena via Engineering Emergent Magnetic Fields in Topological Magnets (Springer Theses)
by Yukako FujishiroThis book addresses novel electronic and thermoelectronic properties arising from topological spin textures as well as topologically non-trivial electronic structures. In particular, it focuses on a unique topological spin texture, i.e., spin hedgehog lattice, emerging in a chiral magnet and explore its novel properties which are distinct from the conventional skyrmion lattice, and discusses the possibility of realizing high-temperature quantum anomalous Hall effect through quantum confinement effect in topological semimetal. This book benefits students and researchers working in the field of condensed matter physics, through providing comprehensive understanding of the current status and the outlook in the field of topological magnets.
Explorations in Computing: An Introduction to Computer Science (Chapman & Hall/CRC Textbooks in Computing)
by John S. ConeryBased on the author's introductory course at the University of Oregon, Explorations in Computing: An Introduction to Computer Science focuses on the fundamental idea of computation and offers insight into how computation is used to solve a variety of interesting and important real-world problems. Taking an active learning approach, the text encoura
Explorations in Computing: An Introduction to Computer Science and Python Programming (Chapman & Hall/CRC Textbooks in Computing)
by John S. ConeryAn Active Learning Approach to Teaching the Main Ideas in Computing Explorations in Computing: An Introduction to Computer Science and Python Programming teaches computer science students how to use programming skills to explore fundamental concepts and computational approaches to solving problems. Tbook gives beginning students an introduction to
Explorations in Monte Carlo Methods (Undergraduate Texts in Mathematics)
by Ronald W. Shonkwiler Franklin MendivilMonte Carlo Methods are among the most used, and useful, computational tools available today. They provide efficient and practical algorithms to solve a wide range of scientific and engineering problems in dozens of areas many of which are covered in this text. These include simulation, optimization, finance, statistical mechanics, birth and death processes, Bayesian inference, quadrature, gambling systems and more.This text is for students of engineering, science, economics and mathematics who want to learn about Monte Carlo methods but have only a passing acquaintance with probability theory. The probability needed to understand the material is developed within the text itself in a direct manner using Monte Carlo experiments for reinforcement. There is a prerequisite of at least one year of calculus and a semester of matrix algebra.Each new idea is carefully motivated by a realistic problem, thus leading to insights into probability theory via examples and numerical simulations. Programming exercises are integrated throughout the text as the primary vehicle for learning the material. All examples in the text are coded in Python as a representative language; the logic is sufficiently clear so as to be easily translated into any other language. Further, Python scripts for each worked example are freely accessible for each chapter. Along the way, most of the basic theory of probability is developed in order to illuminate the solutions to the questions posed. One of the strongest features of the book is the wealth of completely solved example problems. These provide the reader with a sourcebook to follow towards the solution of their own computational problems. Each chapter ends with a large collection of homework problems illustrating and directing the material. This book is suitable as a textbook for students of engineering, finance, and the sciences as well as mathematics. The problem-oriented approach makes it ideal for an applied course in basic probability as well as for a more specialized course in Monte Carlo Methods. Topics include probability distributions, probability calculations, sampling, counting combinatorial objects, Markov chains, random walks, simulated annealing, genetic algorithms, option pricing, gamblers ruin, statistical mechanics, random number generation, Bayesian Inference, Gibbs Sampling and Monte Carlo integration.
Explorations in Quantum Computing
by Colin P. WilliamsBy the year 2020, the basic memory components of a computer will be the size of individual atoms. At such scales, the current theory of computation will become invalid. "Quantum computing" is reinventing the foundations of computer science and information theory in a way that is consistent with quantum physics - the most accurate model of reality currently known. Remarkably, this theory predicts that quantum computers can perform certain tasks breathtakingly faster than classical computers - and, better yet, can accomplish mind-boggling feats such as teleporting information, breaking supposedly "unbreakable" codes, generating true random numbers, and communicating with messages that betray the presence of eavesdropping. This widely anticipated second edition of Explorations in Quantum Computing explains these burgeoning developments in simple terms, and describes the key technological hurdles that must be overcome to make quantum computers a reality. This easy-to-read, time-tested, and comprehensive textbook provides a fresh perspective on the capabilities of quantum computers, and supplies readers with the tools necessary to make their own foray into this exciting field. Topics and features: concludes each chapter with exercises and a summary of the material covered; provides an introduction to the basic mathematical formalism of quantum computing, and the quantum effects that can be harnessed for non-classical computation; discusses the concepts of quantum gates, entangling power, quantum circuits, quantum Fourier, wavelet, and cosine transforms, and quantum universality, computability, and complexity; examines the potential applications of quantum computers in areas such as search, code-breaking, solving NP-Complete problems, quantum simulation, quantum chemistry, and mathematics; investigates the uses of quantum information, including quantum teleportation, superdense coding, quantum data compression, quantum cloning, quantum negation, and quantum cryptography; reviews the advancements made towards practical quantum computers, covering developments in quantum error correction and avoidance, and alternative models of quantum computation. This text/reference is ideal for anyone wishing to learn more about this incredible, perhaps "ultimate," computer revolution. Dr. Colin P. Williams is Program Manager for Advanced Computing Paradigms at the NASA Jet Propulsion Laboratory, California Institute of Technology, and CEO of Xtreme Energetics, Inc. an advanced solar energy company. Dr. Williams has taught quantum computing and quantum information theory as an acting Associate Professor of Computer Science at Stanford University. He has spent over a decade inspiring and leading high technology teams and building business relationships with and Silicon Valley companies. Today his interests include terrestrial and Space-based power generation, quantum computing, cognitive computing, computational material design, visualization, artificial intelligence, evolutionary computing, and remote olfaction. He was formerly a Research Scientist at Xerox PARC and a Research Assistant to Prof. Stephen W. Hawking, Cambridge University.
Explorations in the Mathematics of Data Science: The Inaugural Volume of the Center for Approximation and Mathematical Data Analytics (Applied and Numerical Harmonic Analysis)
by Simon Foucart Stephan WojtowytschThis edited volume reports on the recent activities of the new Center for Approximation and Mathematical Data Analytics (CAMDA) at Texas A&M University. Chapters are based on talks from CAMDA’s inaugural conference – held in May 2023 – and its seminar series, as well as work performed by members of the Center. They showcase the interdisciplinary nature of data science, emphasizing its mathematical and theoretical foundations, especially those rooted in approximation theory.
Exploratory Data Analysis Using R (Chapman and Hall/CRC Data Mining and Knowledge Discovery Series)
by Ronald K. PearsonExploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing. The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available.
Exploratory Data Analysis in Business and Economics
by Thomas CleffIn a world in which we are constantly surrounded by data, figures, and statistics, it is imperative to understand and to be able to use quantitative methods. Statistical models and methods are among the most important tools in economic analysis, decision-making and business planning. This textbook, "Exploratory Data Analysis in Business and Economics", aims to familiarise students of economics and business as well as practitioners in firms with the basic principles, techniques, and applications of descriptive statistics and data analysis. Drawing on practical examples from business settings, it demonstrates the basic descriptive methods of univariate and bivariate analysis. The textbook covers a range of subject matter, from data collection and scaling to the presentation and univariate analysis of quantitative data, and also includes analytic procedures for assessing bivariate relationships. It does not confine itself to presenting descriptive statistics, but also addresses the use of computer programmes such as Excel, SPSS, and STATA, thus treating all of the topics typically covered in a university course on descriptive statistics. The German edition of this textbook is one of the "bestsellers" on the German market for literature in statistics.
Exploratory Data Analysis with Python Cookbook: Over 50 recipes to analyze, visualize, and extract insights from structured and unstructured data
by Ayodele OluleyeExtract valuable insights from data by leveraging various analysis and visualization techniques with this comprehensive guide Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesGain practical experience in conducting EDA on a single variable of interest in PythonLearn the different techniques for analyzing and exploring tabular, time series, and textual data in PythonGet well versed in data visualization using leading Python libraries like Matplotlib and seabornBook DescriptionIn today's data-centric world, the ability to extract meaningful insights from vast amounts of data has become a valuable skill across industries. Exploratory Data Analysis (EDA) lies at the heart of this process, enabling us to comprehend, visualize, and derive valuable insights from various forms of data. This book is a comprehensive guide to Exploratory Data Analysis using the Python programming language. It provides practical steps needed to effectively explore, analyze, and visualize structured and unstructured data. It offers hands-on guidance and code for concepts such as generating summary statistics, analyzing single and multiple variables, visualizing data, analyzing text data, handling outliers, handling missing values and automating the EDA process. It is suited for data scientists, data analysts, researchers or curious learners looking to gain essential knowledge and practical steps for analyzing vast amounts of data to uncover insights. Python is an open-source general purpose programming language which is used widely for data science and data analysis given its simplicity and versatility. It offers several libraries which can be used to clean, analyze, and visualize data. In this book, we will explore popular Python libraries such as Pandas, Matplotlib, and Seaborn and provide workable code for analyzing data in Python using these libraries. By the end of this book, you will have gained comprehensive knowledge about EDA and mastered the powerful set of EDA techniques and tools required for analyzing both structured and unstructured data to derive valuable insights.What you will learnPerform EDA with leading python data visualization librariesExecute univariate, bivariate and multivariate analysis on tabular dataUncover patterns and relationships within time series dataIdentify hidden patterns within textual dataLearn different techniques to prepare data for analysisOvercome challenge of outliers and missing values during data analysisLeverage automated EDA for fast and efficient analysisWho this book is forWhether you are a data analyst, data scientist, researcher or a curious learner looking to analyze structured and unstructured data, this book will appeal to you. It aims to empower you with essential knowledge and practical skills for analyzing and visualizing data to uncover insights. It covers several EDA concepts and provides hands-on instructions on how these can be applied using various Python libraries. Familiarity with basic statistical concepts and foundational knowledge of python programming will help you understand the content better and maximize your learning experience.
Exploratory Data Analytics for Healthcare (Innovations in Big Data and Machine Learning)
by R. Lakshmana KumarExploratory data analysis helps to recognize natural patterns hidden in the data. This book describes the tools for hypothesis generation by visualizing data through graphical representation and provides insight into advanced analytics concepts in an easy way. The book addresses the complete data visualization technologies workflow, explores basic and high-level concepts of computer science and engineering in medical science, and provides an overview of the clinical scientific research areas that enables smart diagnosis equipment. It will discuss techniques and tools used to explore large volumes of medical data and offers case studies that focus on the innovative technological upgradation and challenges faced today. The primary audience for the book includes specialists, researchers, graduates, designers, experts, physicians, and engineers who are doing research in this domain.
Exploratory Programming for the Arts and Humanities
by Nick MontfortThis book introduces programming to readers with a background in the arts and humanities; there are no prerequisites, and no knowledge of computation is assumed. In it, Nick Montfort reveals programming to be not merely a technical exercise within given constraints but a tool for sketching, brainstorming, and inquiring about important topics. He emphasizes programming's exploratory potential -- its facility to create new kinds of artworks and to probe data for new ideas. The book is designed to be read alongside the computer, allowing readers to program while making their way through the chapters. It offers practical exercises in writing and modifying code, beginning on a small scale and increasing in substance. In some cases, a specification is given for a program, but the core activities are a series of "free projects," intentionally underspecified exercises that leave room for readers to determine their own direction and write different sorts of programs. Throughout the book, Montfort also considers how computation and programming are culturally situated -- how programming relates to the methods and questions of the arts and humanities. The book uses Python and Processing, both of which are free software, as the primary programming languages.