Python machine learning

Providing a detailed and pedagogical account of the rapidly-growing field of computational statistical physics, this book covers both the theoretical foundations of equilibrium and non-equilibrium statistical physics, and also modern, computational applications such as percolation, random walks, magnetic systems, machine learning dynamics, and spreading processes on complex networks. A detailed discussion of molecular dynamics simulations is also included, a topic of great importance in biophysics and physical chemistry. The accessible and self-contained approach adopted by the authors makes this book suitable for teaching courses at graduate level, and numerous worked examples and end of chapter problems allow students to test their progress and understanding.

Dealing with all aspects of Monte Carlo simulation of complex physical systems encountered in condensed matter physics and statistical mechanics, this book provides an introduction to computer simulations in physics. The 5th edition contains extensive new material describing numerous powerful algorithms and methods that represent recent developments in the field. New topics such as active matter and machine learning are also introduced. Throughout, there are many applications, examples, recipes, case studies, and exercises to help the reader fully comprehend the material. This book is ideal for graduate students and researchers, both in academia and industry, who want to learn techniques that have become a third tool of physical science, complementing experiment and analytical theory.

What does a probabilistic program actually compute? How can one formally reason about such probabilistic programs? This valuable guide covers such elementary questions and more. It provides a state-of-the-art overview of the theoretical underpinnings of modern probabilistic programming and their applications in machine learning, security, and other domains, at a level suitable for graduate students and non-experts in the field. In addition, the book treats the connection between probabilistic programs and mathematical logic, security (what is the probability that software leaks confidential information?), and presents three programming languages for different applications: Excel tables, program testing, and approximate computing. This title is also available as Open Access on Cambridge Core.

The first book on 6G wireless presents an overall vision for 6G - an era of intelligence-of-everything - with drivers, key capabilities, use cases, KPIs, and the technology innovations that will shape it. These innovations include immersive human-centric communication, sensing, localization, and imaging, connected machine learning and networked AI, Industry 4.0 and beyond with connected intelligence, smart cities and life, and the satellite mega-constellation for 3D full-Earth wireless coverage. Also covered are new air-interface and networking technologies, integrated sensing and communications, and integrated terrestrial and non-terrestrial networks. In addition, novel network architectures to enable network AI, user centric networks, native trustworthiness are discussed. Essential reading for researchers in academia and industry working on B5G wireless communications.

Greatly expanded, this new edition requires only an elementary background in discrete mathematics and offers a comprehensive introduction to the role of randomization and probabilistic techniques in modern computer science. Newly added chapters and sections cover topics including normal distributions, sample complexity, VC dimension, Rademacher complexity, power laws and related distributions, cuckoo hashing, and the Lovasz Local Lemma. Material relevant to machine learning and big data analysis enables students to learn modern techniques and applications. Among the many new exercises and examples are programming-related exercises that provide students with excellent training in solving relevant problems. This book provides an indispensable teaching tool to accompany a one- or two-semester course for advanced undergraduate students in computer science and applied mathematics.

Reproducing kernel Hilbert spaces have developed into an important tool in many areas, especially statistics and machine learning, and they play a valuable role in complex analysis, probability, group representation theory, and the theory of integral operators. This unique text offers a unified overview of the topic, providing detailed examples of applications, as well as covering the fundamental underlying theory, including chapters on interpolation and approximation, Cholesky and Schur operations on kernels, and vector-valued spaces. Self-contained and accessibly written, with exercises at the end of each chapter, this unrivalled treatment of the topic serves as an ideal introduction for graduate students across mathematics, computer science, and engineering, as well as a useful reference for researchers working in functional analysis or its applications.

This monograph presents a visually striking exploration of Refik Anadol’s data-driven installation, created for the opening of the new BRUSK museum in Bruges. The book offers exclusive visuals and reflections on the intersection of art, technology, and memory in Anadol’s work.Working at the intersection of art, science, and technology, Refik Anadol is best known for his large-scale immersive installations and public artworks that use machine learning, AI, and big data to visualize complex systems. Anadol’s work transforms raw data – ranging from urban landscapes to brainwaves – into dynamic, multi-sensory experiences, pushing the boundaries of how we perceive space, memory, and reality.For the inaugural programme at BRUSK, the new museum for contemporary art in Bruges, Belgium, Anadol presents a major site-specific installation that immerses visitors in a datadriven landscape unique to the city.

In the last few years, Algorithms for Convex Optimization have revolutionized algorithm design, both for discrete and continuous optimization problems. For problems like maximum flow, maximum matching, and submodular function minimization, the fastest algorithms involve essential methods such as gradient descent, mirror descent, interior point methods, and ellipsoid methods. The goal of this self-contained book is to enable researchers and professionals in computer science, data science, and machine learning to gain an in-depth understanding of these algorithms. The text emphasizes how to derive key algorithms for convex optimization from first principles and how to establish precise running time bounds. This modern text explains the success of these algorithms in problems of discrete optimization, as well as how these methods have significantly pushed the state of the art of convex optimization itself.

Mood disorders such as depression and bipolar disorder are common mental illnesses, affecting millions of patients worldwide. The application of newly available brain imaging methods to the study of mood disorders holds substantial promise in uncovering the brain mechanisms affected in these illnesses. This comprehensive and authoritative text features contributions from leading international experts, providing easily accessible information on the study of the brain mechanisms involved in the causation of mood disorders and the available treatments. Topics covered include the potential of magnetoencephalography (MEG), neuroimaging brain inflammation in depression, electrophysiology studies in mood disorders, and the applications of machine learning, filling an important gap in available neuropsychiatric literature and highlighting new developments. An invaluable resource for practitioners in the fields of psychiatry, neurology, primary care medicine, and related mental health professio

This book integrates philosophy of science, data acquisition methods, and statistical modeling techniques to present readers with a forward-thinking perspective on clinical science. It reviews modern research practices in clinical psychology that support the goals of psychological science, study designs that promote good research, and quantitative methods that can test specific scientific questions. It covers new themes in research including intensive longitudinal designs, neurobiology, developmental psychopathology, and advanced computational methods such as machine learning. Core chapters examine significant statistical topics, for example missing data, causality, meta-analysis, latent variable analysis, and dyadic data analysis. A balanced overview of observational and experimental designs is also supplied, including preclinical research and intervention science. This is a foundational resource that supports the methodological training of the current and future generations of clinic

This groundbreaking textbook combines straightforward explanations with a wealth of practical examples to offer an innovative approach to teaching linear algebra. Requiring no prior knowledge of the subject, it covers the aspects of linear algebra - vectors, matrices, and least squares - that are needed for engineering applications, discussing examples across data science, machine learning and artificial intelligence, signal and image processing, tomography, navigation, control, and finance. The numerous practical exercises throughout allow students to test their understanding and translate their knowledge into solving real-world problems, with lecture slides, additional computational exercises in Julia and MATLAB®, and data sets accompanying the book online. Suitable for both one-semester and one-quarter courses, as well as self-study, this self-contained text provides beginning students with the foundation they need to progress to more advanced study.

A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding bot

Providing a concise and accessible overview of the design, implementation and management of medical software, this textbook will equip students with a solid understanding of critical considerations for both standalone medical software (software as a medical device/SaMD) and software that is integrated into hardware devices. It includes: practical discussion of key regulatory documents and industry standards, and how these translate into concrete considerations for medical software design; detailed coverage of the medical software lifecycle process ; accessible introduction to quality and risk management systems in the context of medical software; succinct coverage of essential topics in data science, machine learning, statistics, cybersecurity, software engineering and healthcare bring readers up-to-speed; six cautionary real-world case studies illustrate the dangers of improper or careless software processes. Accompanied by online resources for instructors, this is the ideal introduct

The dramatic increase in computer performance has been extraordinary, but not for all computations: it has key limits and structure. Software architects, developers, and even data scientists need to understand how exploit the fundamental structure of computer performance to harness it for future applications. Ideal for upper level undergraduates, Computer Architecture for Scientists covers four key pillars of computer performance and imparts a high-level basis for reasoning with and understanding these concepts: Small is fast – how size scaling drives performance; Implicit parallelism – how a sequential program can be executed faster with parallelism; Dynamic locality – skirting physical limits, by arranging data in a smaller space; Parallelism – increasing performance with teams of workers. These principles and models provide approachable high-level insights and quantitative modelling without distracting low-level detail. Finally, the text covers the GPU and machine-learning accelerat

An intuitive and accessible text explaining the fundamentals and applications of graph signal processing. Requiring only an elementary understanding of linear algebra, it covers both basic and advanced topics, including node domain processing, graph signal frequency, sampling, and graph signal representations, as well as how to choose a graph. Understand the basic insights behind key concepts and learn how graphs can be associated to a range of specific applications across physical, biological and social networks, distributed sensor networks, image and video processing, and machine learning. With numerous exercises and Matlab examples to help put knowledge into practice, and a solutions manual available online for instructors, this unique text is essential reading for graduate and senior undergraduate students taking courses on graph signal processing, signal processing, information processing, and data analysis, as well as researchers and industry professionals.

Malicious hackers utilize the World Wide Web to share knowledge. Analyzing the online communication of these threat actors can help reduce the risk of attacks. This book shifts attention from the defender environment to the attacker environment, offering a new security paradigm of 'proactive cyber threat intelligence' that allows defenders of computer networks to gain a better understanding of their adversaries by analyzing assets, capabilities, and interest of malicious hackers. The authors propose models, techniques, and frameworks based on threat intelligence mined from the heart of the underground cyber world: the malicious hacker communities. They provide insights into the hackers themselves and the groups they form dynamically in the act of exchanging ideas and techniques, buying or selling malware, and exploits. The book covers both methodology - a hybridization of machine learning, artificial intelligence, and social network analysis methods - and the resulting conclusions, det

Covering both quantitative and qualitative methods, this book examines the breadth of modern market research methods for upper level students across business schools and social science faculties. Modern and trending topics including social networks, machine learning, big data, and artificial intelligence are addressed and real world examples and case studies illustrate the application of the methods. This text examines potential problems, such as researcher bias, and discusses effective solutions in the preparation of research reports and papers, and oral presentations. Assuming no prior knowledge of statistics or econometrics, discrete chapters offer a clear introduction to both, opening up the quantitative methods to all students. Each chapter contains rigorous academic theory, including a synthesis of the recent literature as well as key historical references, applied contextualization and recent research results, making it an excellent resource for practitioners. Online resources i

Imagine a world where machines can see and understand the world the way humans do. Rapid progress in artificial intelligence has led to smartphones that recognize faces, cars that detect pedestrians, and algorithms that suggest diagnoses from clinical images, among many other applications. The success of computer vision is founded on a deep understanding of the neural circuits in the brain responsible for visual processing. This book introduces the neuroscientific study of neuronal computations in visual cortex alongside of the psychological understanding of visual cognition and the burgeoning field of biologically-inspired artificial intelligence. Topics include the neurophysiological investigation of visual cortex, visual illusions, visual disorders, deep convolutional neural networks, machine learning, and generative adversarial networks among others. It is an ideal resource for students and researchers looking to build bridges across different approaches to studying and developing

With this groundbreaking text, discover how wireless artificial intelligence (AI) can be used to determine position at centimeter level, sense motion and vital signs, and identify events and people. Using a highly innovative approach that employs existing wireless equipment and signal processing techniques to turn multipaths into virtual antennas, combined with the physical principle of time reversal and machine learning, it covers fundamental theory, extensive experimental results, and real practical use cases developed for products and applications. Topics explored include indoor positioning and tracking, wireless sensing and analytics, wireless power transfer and energy efficiency, 5G and next-generation communications, and the connection of large numbers of heterogeneous IoT devices of various bandwidths and capabilities. Demo videos accompanying the book online enhance understanding of these topics. Providing a unified framework for wireless AI, this is an excellent text for gradu

Stochastic differential equations are differential equations whose solutions are stochastic processes. They exhibit appealing mathematical properties that are useful in modeling uncertainties and noisy phenomena in many disciplines. This book is motivated by applications of stochastic differential equations in target tracking and medical technology and, in particular, their use in methodologies such as filtering, smoothing, parameter estimation, and machine learning. It builds an intuitive hands-on understanding of what stochastic differential equations are all about, but also covers the essentials of Itô calculus, the central theorems in the field, and such approximation schemes as stochastic Runge–Kutta. Greater emphasis is given to solution methods than to analysis of theoretical properties of the equations. The book's practical approach assumes only prior understanding of ordinary differential equations. The numerous worked examples and end-of-chapter exercises include application-

With this comprehensive and accessible introduction to the field, you will gain all the skills and knowledge needed to work with current and future audio, speech, and hearing processing technologies. Topics covered include mobile telephony, human-computer interfacing through speech, medical applications of speech and hearing technology, electronic music, audio compression and reproduction, big data audio systems and the analysis of sounds in the environment. All of this is supported by numerous practical illustrations, exercises, and hands-on MATLAB® examples on topics as diverse as psychoacoustics (including some auditory illusions), voice changers, speech compression, signal analysis and visualisation, stereo processing, low-frequency ultrasonic scanning, and machine learning techniques for big data. With its pragmatic and application driven focus, and concise explanations, this is an essential resource for anyone who wants to rapidly gain a practical understanding of speech and audi

Utilising both key mathematical tools and state-of-the-art research results, this text explores the principles underpinning large-scale information processing over networks and examines the crucial interaction between big data and its associated communication, social and biological networks. Written by experts in the diverse fields of machine learning, optimisation, statistics, signal processing, networking, communications, sociology and biology, this book employs two complementary approaches: first analysing how the underlying network constrains the upper-layer of collaborative big data processing, and second, examining how big data processing may boost performance in various networks. Unifying the broad scope of the book is the rigorous mathematical treatment of the subjects, which is enriched by in-depth discussion of future directions and numerous open-ended problems that conclude each chapter. Readers will be able to master the fundamental principles for dealing with big data over

The advent of advanced astronomical instruments and huge surveys means that the twenty-first century is witnessing a rapid growth in astrostatistical science. Interpreting the cosmic microwave background, weak and strong gravitational lensing, galaxy clustering and other signatures of the early Universe all require advanced statistical techniques. Led by members of the IAU's newly formed Working Group in Astrostatistics and Astroinformatics, IAU Symposium 306 emphasises the intricate mathematical methods needed to extract scientific insights from large and complicated datasets. It contains contributions on Bayesian methods, weak lensing cosmology, CMB data analysis, cross-correlating datasets, large-scale structure, data mining and machine learning, ongoing surveys and the future Euclid mission. The approaches presented here provide a solid foundation to advance new research methods in cosmology, making it an essential text for the large community of astronomers and statisticians who w

Providing a novel approach to sparsity, this comprehensive book presents the theory of stochastic processes that are ruled by linear stochastic differential equations, and that admit a parsimonious representation in a matched wavelet-like basis. Two key themes are the statistical property of infinite divisibility, which leads to two distinct types of behaviour - Gaussian and sparse - and the structural link between linear stochastic processes and spline functions, which is exploited to simplify the mathematical analysis. The core of the book is devoted to investigating sparse processes, including a complete description of their transform-domain statistics. The final part develops practical signal-processing algorithms that are based on these models, with special emphasis on biomedical image reconstruction. This is an ideal reference for graduate students and researchers with an interest in signal/image processing, compressed sensing, approximation theory, machine learning, or statistic

The principles of cognition are becoming increasingly important in the areas of signal processing, communications and control. In this groundbreaking book, Simon Haykin, a pioneer in the field and an award-winning researcher, educator and author, sets out the fundamental ideas of cognitive dynamic systems. Weaving together the various branches of study involved, he demonstrates the power of cognitive information processing and highlights a range of future research directions. The book begins with a discussion of core topics such as cognition and sensing, dealing, in particular, with the perception-action cycle. Bayesian filtering, machine learning and dynamic programming are then addressed. Building on these foundations, there is detailed coverage of two important practical applications, cognitive radar and cognitive radio. Blending theory and practice, this insightful book is aimed at all graduate students and researchers looking for a thorough grounding in this fascinating field.

Relational mathematics is to operations research and informatics what numerical mathematics is to engineering: it is intended to help modelling, reasoning, and computing. Its applications are therefore diverse, ranging from psychology, linguistics, decision aid, and ranking to machine learning and spatial reasoning. Although many developments have been made in recent years, they have rarely been shared amongst this broad community of researchers. This comprehensive 2010 overview begins with an easy introduction to the topic, assuming a minimum of prerequisites; but it is nevertheless theoretically sound and up to date. It is suitable for applied scientists, explaining all the necessary mathematics from scratch using a multitude of visualised examples, via matrices and graphs. It ends with tangible results on the research level. The author illustrates the theory and demonstrates practical tasks in operations research, social sciences and the humanities.

This book equips the reader to understand every important aspect of the dynamics of rotating machines. Will the vibration be large? What influences machine stability? How can the vibration be reduced? Which sorts of rotor vibration are the worst? The book develops this understanding initially using extremely simple models for each phenomenon, in which (at most) four equations capture the behavior. More detailed models are then developed based on finite element analysis, to enable the accurate simulation of the relevant phenomena for real machines. Analysis software (in MATLAB) is associated with this book, and novices to rotordynamics can expect to make good predictions of critical speeds and rotating mode shapes within days. The book is structured more as a learning guide than as a reference tome and provides readers with more than 100 worked examples and more than 100 problems and solutions.

Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.

Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.

What happens to risk as the economic horizon goes to zero and risk is seen as an exposure to a change in state that may occur instantaneously at any time? All activities that have been undertaken statically at a fixed finite horizon can now be reconsidered dynamically at a zero time horizon, with arrival rates at the core of the modeling. This book, aimed at practitioners and researchers in financial risk, delivers the theoretical framework and various applications of the newly established dynamic conic finance theory. The result is a nonlinear non-Gaussian valuation framework for risk management in finance. Risk-free assets disappear and low risk portfolios must pay for their risk reduction with negative expected returns. Hedges may be constructed to enhance value by exploiting risk interactions. Dynamic trading mechanisms are synthesized by machine learning algorithms. Optimal exposures are designed for option positioning simultaneously across all strikes and maturities.

Marketing and sales prioritize AI and machine learning more than any other business department, yet often struggle with how to scale and strategize the opportunities it presents.AI Strategy for Sales and Marketing presents a framework for understanding how AI can boost customer-centricity and sales by creating a connected strategy that delivers value today and into the future. Supported by practical tips and advice throughout, it covers topics including personalization, upskilling, customer experience for both on and offline shopping channels and the importance of using AI responsibly to create consumer trust. Featuring original research and interviews with leading practitioners, it also contains global case studies from organizations in a range of sectors, including Samsung, LinkedIn, PwC, Rolls Royce and Hilton, with insights into the various stages of their adoption journeys. Written by a recognized industry expert, it is an invaluable resource for those wanting to benefit from usin

There are no silver bullets in algorithm design, and no single algorithmic idea is powerful and flexible enough to solve every computational problem. Nor are there silver bullets in algorithm analysis, as the most enlightening method for analyzing an algorithm often depends on the problem and the application. However, typical algorithms courses rely almost entirely on a single analysis framework, that of worst-case analysis, wherein an algorithm is assessed by its worst performance on any input of a given size. The purpose of this book is to popularize several alternatives to worst-case analysis and their most notable algorithmic applications, from clustering to linear programming to neural network training. Forty leading researchers have contributed introductions to different facets of this field, emphasizing the most important models and results, many of which can be taught in lectures to beginning graduate students in theoretical computer science and machine learning.

Algorithms permeate our lives in numerous ways, performing tasks that until recently could only be carried out by humans. Artificial Intelligence (AI) technologies, based on machine learning algorithms and big-data-powered systems, can perform sophisticated tasks such as driving cars, analyzing medical data, and evaluating and executing complex financial transactions - often without active human control or supervision. Algorithms also play an important role in determining retail pricing, online advertising, loan qualification, and airport security. In this work, Martin Ebers and Susana Navas bring together a group of scholars and practitioners from across Europe and the US to analyze how this shift from human actors to computers presents both practical and conceptual challenges for legal and regulatory systems. This book should be read by anyone interested in the intersection between computer science and law, how the law can better regulate algorithmic design, and the legal ramificatio

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction

Classical computer science textbooks tell us that some problems are 'hard'. Yet many areas, from machine learning and computer vision to theorem proving and software verification, have defined their own set of tools for effectively solving complex problems. Tractability provides an overview of these different techniques, and of the fundamental concepts and properties used to tame intractability. This book will help you understand what to do when facing a hard computational problem. Can the problem be modelled by convex, or submodular functions? Will the instances arising in practice be of low treewidth, or exhibit another specific graph structure that makes them easy? Is it acceptable to use scalable, but approximate algorithms? A wide range of approaches is presented through self-contained chapters written by authoritative researchers on each topic. As a reference on a core problem in computer science, this book will appeal to theoreticians and practitioners alike.

Originating from the authors' own graduate course at the University of North Carolina, this material has been thoroughly tried and tested over many years, making the book perfect for a two-term course or for self-study. It provides a concise introduction that covers all of the measure theory and probability most useful for statisticians, including Lebesgue integration, limit theorems in probability, martingales, and some theory of stochastic processes. Readers can test their understanding of the material through the 300 exercises provided. The book is especially useful for graduate students in statistics and related fields of application (biostatistics, econometrics, finance, meteorology, machine learning, and so on) who want to shore up their mathematical foundation. The authors establish common ground for students of varied interests which will serve as a firm 'take-off point' for them as they specialize in areas that exploit mathematical machinery.

Inferring the precise locations and splicing patterns of genes in DNA is a difficult but important task, with broad applications to biomedicine. The mathematical and statistical techniques that have been applied to this problem are surveyed and organized into a logical framework based on the theory of parsing. Both established approaches and methods at the forefront of current research are discussed. Numerous case studies of existing software systems are provided, in addition to detailed examples that work through the actual implementation of effective gene-predictors using hidden Markov models and other machine-learning techniques. Background material on probability theory, discrete mathematics, computer science, and molecular biology is provided, making the book accessible to students and researchers from across the life and computational sciences. This book is ideal for use in a first course in bioinformatics at graduate or advanced undergraduate level, and for anyone wanting to kee

Advances in experimental methods have resulted in the generation of enormous volumes of data across the life sciences. Hence clustering and classification techniques that were once predominantly the domain of ecologists are now being used more widely. This 2006 book provides an overview of these important data analysis methods, from long-established statistical methods to more recent machine learning techniques. It aims to provide a framework that will enable the reader to recognise the assumptions and constraints that are implicit in all such techniques. Important generic issues are discussed first and then the major families of algorithms are described. Throughout the focus is on explanation and understanding and readers are directed to other resources that provide additional mathematical rigour when it is required. Examples taken from across the whole of biology, including bioinformatics, are provided throughout the book to illustrate the key concepts and each technique's potential.

Text mining is a new and exciting area of computer science research that tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. Similarly, link detection – a rapidly evolving approach to the analysis of text that shares and builds upon many of the key elements of text mining – also provides new tools for people to better leverage their burgeoning textual data resources. The Text Mining Handbook presents a comprehensive discussion of the state-of-the-art in text mining and link detection. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, the book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection in such varied fields a

The entanglements of people and data, code and space, knowledge and power: how data and algorithms shape the world―and shape us within that world.With the emergence of a post-truth world, we have witnessed the dissolution of the common ground on which truth claims were negotiated, individual agency enacted, and public spheres shaped. What happens when, as Nietzsche claimed, there are no facts, but only interpretations? In this book, Mark Shepard examines the entanglements of people and data, code and space, knowledge and power that have produced an uncommon ground―a disaggregated public sphere where the extraction of behavioral data and their subsequent processing and sale have led to the emergence of micropublics of ever-finer granularity. Shepard explores how these new post-truth territories are propagated through machine learning systems and social networks, which shape the public and private spaces of everyday life. He traces the balkanization and proliferation of online news and t