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Algorithms : Technology, Culture, Politics
Algorithms: Technology, Culture, Politics develops a relational, situated approach to algorithms.It takes a middle ground between theories that give the algorithm a singular and stable meaning in using it as a central analytic category for contemporary society and theories that dissolve the term into the details of empirical studies. The book discusses algorithms in relation to hardware and material conditions, code, data, and subjects such as users, programmers, but also “data doubles”.The individual chapters bridge critical discussions on bias, exclusion, or responsibility with the necessary detail on the contemporary state of information technology.The examples include state-of-the-art applications of machine learning, such as self-driving cars, and large language models such as GPT. The book will be of interest for everyone engaging critically with algorithms, particularly in the social sciences, media studies, STS, political theory, or philosophy.With its broad scope it can serve as a high-level introduction that picks up and builds on more than two decades of critical research on algorithms.
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Algorithms for Data Science
This textbook on practical data analytics unites fundamental principles, algorithms, and data.Algorithms are the keystone of data analytics and the focal point of this textbook.Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent.But practical data analytics requires more than just the foundations.Problems and data are enormously variable and only the most elementary of algorithms can be used without modification.Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis.By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction.The second chapter introduces associative statistics, themathematical foundation of scalable algorithms and distributed computing.Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II.The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics.The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail.A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science.The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty.The core material of every chapter is accessible to all with these prerequisites.The chapters often expand at the close with innovations of interest to practitioners of data science.Each chapter includes exercises of varying levels of difficulty.The text is eminently suitable for self-study and an exceptional resource for practitioners.
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Graph Algorithms for Data Science
Graphs are the natural way to understand connected data.This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment. In Graph Algorithms for Data Science you will learn: Labeled-property graph modelingConstructing a graph from structured data such as CSV or SQLNLP techniques to construct a graph from unstructured dataCypher query language syntax to manipulate data and extract insightsSocial network analysis algorithms like PageRank and community detectionHow to translate graph structure to a ML model input with node embedding modelsUsing graph features in node classification and link prediction workflows Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis.It's filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs.You'll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more.You don't need any graph experience to start benefiting from this insightful guide.These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects. about the technology Graphs reveal the relationships in your data.Tracking these interlinking connections reveals new insights and influences and lets you analyze each data point as part of a larger whole.This interconnected data is perfect for machine learning, as well as analyzing social networks, communities, and even product recommendations. about the book Graph Algorithms for Data Science teaches you how to construct graphs from both structured and unstructured data.You'll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights.The book explores common and useful graph algorithms like PageRank and community detection/clustering algorithms.Each new algorithm you learn is instantly put into action to complete a hands-on data project, including modeling a social network!Finally, you'll learn how to utilize graphs to upgrade your machine learning, including utilizing node embedding models and graph neural networks.
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Algorithms
The leading introduction to computer algorithms in use today, including fifty algorithms every programmer should know Princeton Computer Science professors, Robert Sedgewick and Kevin Wayne, survey the most important computer algorithms in use and of interest to anyone working in science, mathematics, and engineering, and those who use computation in the liberal arts.They provide a full treatment of data structures and algorithms for key areas that enable you to confidently implement, debug, and put them to work in any computational environment. Fundamentals: Basic programming modelsData abstractionBags, queues, and stacksAnalysis of algorithms Sorting Elementary sortsMergesortQuicksortPriority queuesApplications Graphs Undirected graphsDirected graphsMinimum spanning treesShortest paths Strings String sortsTriesSubstring searchRegular expressionsData compression These algorithms are generally ingenious creations that, remarkably, can each be expressed in just a dozen or two lines of code.As a group, they represent problem-solving power of amazing scope.They have enabled the construction of computational artifacts, the solution of scientific problems, and the development of commercial applications that would not have been feasible without them.
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Automating the News : How Algorithms Are Rewriting the Media
From hidden connections in big data to bots spreading fake news, journalism is increasingly computer-generated.An expert in computer science and media explains the present and future of a world in which news is created by algorithm. Amid the push for self-driving cars and the roboticization of industrial economies, automation has proven one of the biggest news stories of our time.Yet the wide-scale automation of the news itself has largely escaped attention.In this lively exposé of that rapidly shifting terrain, Nicholas Diakopoulos focuses on the people who tell the stories—increasingly with the help of computer algorithms that are fundamentally changing the creation, dissemination, and reception of the news. Diakopoulos reveals how machine learning and data mining have transformed investigative journalism.Newsbots converse with social media audiences, distributing stories and receiving feedback.Online media has become a platform for A/B testing of content, helping journalists to better understand what moves audiences.Algorithms can even draft certain kinds of stories. These techniques enable media organizations to take advantage of experiments and economies of scale, enhancing the sustainability of the fourth estate.But they also place pressure on editorial decision-making, because they allow journalists to produce more stories, sometimes better ones, but rarely both. Automating the News responds to hype and fears surrounding journalistic algorithms by exploring the human influence embedded in automation.Though the effects of automation are deep, Diakopoulos shows that journalists are at little risk of being displaced.With algorithms at their fingertips, they may work differently and tell different stories than they otherwise would, but their values remain the driving force behind the news.The human–algorithm hybrid thus emerges as the latest embodiment of an age-old tension between commercial imperatives and journalistic principles.
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Online Algorithms
Online algorithms are a rich area of research with widespread applications in scheduling, combinatorial optimization, and resource allocation problems.This lucid textbook provides an easy but rigorous introduction to online algorithms for graduate and senior undergraduate students.In-depth coverage of most of the important topics is presented with special emphasis on elegant analysis.The book starts with classical online paradigms like the ski-rental, paging, list-accessing, bin packing, where performance of online algorithms is studied under the worst-case input and moves on to newer paradigms like 'beyond worst case', where online algorithms are augmented with predictions using machine learning algorithms.The book goes on to cover multiple applied problems such as routing in communication networks, server provisioning in cloud systems, communication with energy harvested from renewable sources, and sub-modular partitioning.Finally, a wide range of solved examples and practice exercises are included, allowing hands-on exposure to the concepts.
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Bandit Algorithms
Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it.This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks.A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics.Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration.The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes.
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Kumon Algorithms
Kumon Algorithms
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