The disciplines of knowledge engineering and knowledge management are closely tied. Knowledge engineering deals with the development of information systems in which knowledge and reasoning play pivotal roles. Knowledge management, a newly developed field at the intersection of computer science and management, deals with knowledge as a key resource in modern organizations. Managing knowledge within an organization is inconceivable without the use of advanced information systems; the design and implementation of such systems pose great organization as well as technical challenges.

Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.

A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.

How networked technology enables the emergence of a new collaborative society. Humans are hard-wired for collaboration, and new technologies of communication act as a super-amplifier of our natural collaborative mindset. This volume in the MIT Press Essential Knowledge series examines the emergence of a new kind of social collaboration enabled by networked technologies. This new collaborative society might be characterized as a series of services and startups that enable peer-to-peer exchanges and interactions though technology. Some believe that the economic aspects of the new collaboration have the potential to make society more equitable; others see collaborative communities based on sharing as a cover for social injustice and user exploitation. The book covers the “sharing economy,” and the hijacking of the term by corporations; different models of peer production, and motivations to participate; collaborative media production and consumption, the definitions of “amateur” and “professional,” and the power of memes; hactivism and social movements, including Anonymous and anti-ACTA protest; collaborative knowledge creation, including citizen science; collaborative self-tracking; and internet-mediated social relations, as seen in the use of Instagram, Snapchat, and Tinder. Finally, the book considers the future of these collaborative tendencies and the disruptions caused by fake news, bots, and other challenges.

A rigorous and comprehensive textbook covering the major approaches to knowledge graphs, an active and interdisciplinary area within artificial intelligence. The field of knowledge graphs, which allows us to model, process, and derive insights from complex real-world data, has emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language processing, data mining, and the semantic web. Current projects involve predicting cyberattacks, recommending products, and even gleaning insights from thousands of papers on COVID-19. This textbook offers rigorous and comprehensive coverage of the field. It focuses systematically on the major approaches, both those that have stood the test of time and the latest deep learning methods.

If machine learning transforms the nature of knowledge, does it also transform the practice of critical thought? Machine learning—programming computers to learn from data—has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing, computer gaming, recommendation systems, finance, surveillance, and robotics use machine learning. Machine learning devices (sometimes understood as scientific models, sometimes as operational algorithms) anchor the field of data science. They have also become mundane mechanisms deeply embedded in a variety of systems and gadgets. In contexts from the everyday to the esoteric, machine learning is said to transform the nature of knowledge. In this book, Adrian Mackenzie investigates whether machine learning also transforms the practice of critical thinking. Mackenzie focuses on machine learners—either humans and machines or human-machine relations—situated among settings, data, and devices. The settings range from fMRI to Facebook; the data anything from cat images to DNA sequences; the devices include neural networks, support vector machines, and decision trees. He examines specific learning algorithms—writing code and writing about code—and develops an archaeology of operations that, following Foucault, views machine learning as a form of knowledge production and a strategy of power. Exploring layers of abstraction, data infrastructures, coding practices, diagrams, mathematical formalisms, and the social organization of machine learning, Mackenzie traces the mostly invisible architecture of one of the central zones of contemporary technological cultures. Mackenzie's account of machine learning locates places in which a sense of agency can take root. His archaeology of the operational formation of machine learning does not unearth the footprint of a strategic monolith but reveals the local tributaries of force that feed into the generalization and plurality of the field.

Why, surrounded by screens and smart devices, we feel a deep connection to the analog—vinyl records, fountain pens, Kodak film, and other nondigital tools. We’re surrounded by screens; our music comes in the form of digital files; we tap words into a notes app. Why do we still crave the “realness” of analog, seeking out vinyl records, fountain pens, cameras with film? In this volume in the MIT Press Essential Knowledge series, Robert Hassan explores our deep connection to analog technology. Our analog urge, he explains, is about what we’ve lost from our technological past, something that’s not there in our digital present. We’re nostalgic for what we remember indistinctly as somehow more real, more human. Surveying some of the major developments of analog technology, Hassan shows us what’s been lost with the digital. Along the way, he discusses the appeal of the 2011 silent, black-and-white Oscar-winning film The Artist; the revival of the non-e-book book; the early mechanical clocks that enforced prayer and worship times; and the programmable loom. He describes the effect of the typewriter on Nietzsche’s productivity, the pivotal invention of the telegraph, and the popularity of the first televisions despite their iffy picture quality. The transition to digital is marked by the downgrading of human participation in the human-technology relationship. We have unwittingly unmoored ourselves, Hassan warns, from the anchors of analog technology and the natural world. Our analog nostalgia is for those ancient aspects of who and what we are.