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Fundamentals of Machine Learning - Thomas P. (professor Of Computer Science Trappenberg - Bog - Oxford University Press - Plusbog.dk

Fundamentals of Machine Learning - Thomas P. (professor Of Computer Science Trappenberg - Bog - Oxford University Press - Plusbog.dk

Interest in machine learning is exploding worldwide, both in research and for industrial applications. Machine learning is fast becoming a fundamental part of everyday life. This book is a brief introduction to this area - exploring its importance in a range of many disciplines, from science to engineering, and even its broader impact on our society. The book is written in a style that strikes a balance between brevity of explanation, rigorous mathematical argument, and outlines principle ideas. At the same time, it provides a comprehensive overview of a variety of methods and their application within this field. This includes an introduction to Bayesian approaches to modeling, as well as deep learning. Writing small programs to apply machine learning techniques is made easy by high level programming systems, and this book shows examples in Python with the machine learning libraries ''sklearn'' and ''Keras''. The first four chapters concentrate on the practical side of applying machine learning techniques. The following four chapters discuss more fundamental concepts that includes their formulation in a probabilistic context. This is followed by two more chapters on advanced models, that of recurrent neural networks and that of reinforcement learning. The book closes with a brief discussion on the impact of machine learning and AI on our society.Fundamentals of Machine Learning provides a brief and accessible introduction to this rapidly growing field, one that will appeal to students and researchers across computer science and computational neuroscience, as well as the broader cognitive sciences.

DKK 440.00
3

Machine Learning for Econometrics - Christophe Gaillac - Bog - Oxford University Press - Plusbog.dk

Machine Learning for Econometrics - Christophe Gaillac - Bog - Oxford University Press - Plusbog.dk

Machine Learning for Econometrics is a book for economists seeking to grasp modern machine learning techniques - from their predictive performance to the revolutionary handling of unstructured data - in order to establish causal relationships from data. The volume covers automatic variable selection in various high-dimensional contexts, estimation of treatment effect heterogeneity, natural language processing (NLP) techniques, as well as synthetic control and macroeconomic forecasting. The foundations of machine learning methods are introduced to provide both a thorough theoretical treatment of how they can be used in econometrics and numerous economic applications, and each chapter contains a series of empirical examples, programs, and exercises to facilitate the reader''s adoption and implementation of the techniques.

DKK 417.00
1

Machine Learning for Econometrics - Christophe Gaillac - Bog - Oxford University Press - Plusbog.dk

Machine Learning for Econometrics - Christophe Gaillac - Bog - Oxford University Press - Plusbog.dk

Machine Learning for Econometrics is a book for economists seeking to grasp modern machine learning techniques - from their predictive performance to the revolutionary handling of unstructured data - in order to establish causal relationships from data. The volume covers automatic variable selection in various high-dimensional contexts, estimation of treatment effect heterogeneity, natural language processing (NLP) techniques, as well as synthetic control and macroeconomic forecasting. The foundations of machine learning methods are introduced to provide both a thorough theoretical treatment of how they can be used in econometrics and numerous economic applications, and each chapter contains a series of empirical examples, programs, and exercises to facilitate the reader''s adoption and implementation of the techniques.

DKK 1195.00
1

Demystifying China's Innovation Machine - David Gann - Bog - Oxford University Press - Plusbog.dk

Demystifying China's Innovation Machine - David Gann - Bog - Oxford University Press - Plusbog.dk

China''s extraordinary economic development is explained in large part by the way it innovates. Contrary to widely held views, China''s innovation machine is not created and controlled by an all-powerful government. Instead, it is a complex, interdependent system composed of various elements, involving bottom-up innovation driven by innovators and entrepreneurs and highly pragmatic and adaptive top-down policy. Using case studies of leading firms and industries, along with statistics and policy analysis, this book argues that China''s innovation machine is similar to a natural ecosystem. Innovations in technology, organization, and business models resemble genetic mutations which are initially random, self-serving, and isolated, but the best fitting are selected by the market and their impacts are amplified by the innovation machine. This machine draws on China''s multitude manufacturers, supply chains, innovation clusters, and digitally literate population, connected through super-sized digital platforms. China''s innovation suffers from a lack of basic research and reliance upon certain critical technologies from overseas, yet its scale (size) and scope (diversity) possess attributes that make it self-correcting and stronger in the face of challenges. China''s innovation machine is most effective in a policy environment where the market prevails; policy intervention plays a significant role when market mechanisms are premature or fail. The future success of China''s innovation will depend on continuing policy pragmatism, mass innovation, and entrepreneurship, and the development of the ''new infrastructures''.

DKK 545.00
3

The Universal Turing Machine - - Bog - Oxford University Press - Plusbog.dk

The Universal Turing Machine - - Bog - Oxford University Press - Plusbog.dk

This volume commemorates the work of Alan Turing, because it was Turing who not only introduced the most persuasive and influential concept of a machine model of effective computability but who also anticipated in his work the diversity of topics brought together here. As Newman put it in his memoir of Turing, "The central problem with which he started and to which he constantly returned is the extent and the limitations of mechanistic explanations of nature."Turing''s paper "On computable numbers, with an application to the Entscheidungs problem" appeared in print in 1937. It contained Turing''s thesis that every `effective'' computation can be programmed on a Turing machine. Furthermore it contained the unsolvability of the halting problem and of the decision problem for first-order logic, and it presented the invention of the universal Turing machine. The publication of this idea is acknowledged as a landmark of the computer age.Part I of the volume explores the historical aspect with essays on the background, on Turing''s work, and on subsequent developments. Part II contains an extensive series of essays on the influence and applications of these ideas in mathematics, mathematical logic, philosophy of mathematics, computer science, artificial intelligence, philosophy of language, philosophy of mind, and physics.

DKK 689.00
3

Machine Learning for Signal Processing - Prof Max A. Little - Bog - Oxford University Press - Plusbog.dk

Machine Learning for Signal Processing - Prof Max A. Little - Bog - Oxford University Press - Plusbog.dk

This book describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Taking a gradual approach, it builds up concepts in a solid, step-by-step fashion so that the ideas and algorithms can be implemented in practical software applications.Digital signal processing (DSP) is one of the ''foundational'' engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance, and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference.DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered, yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in this important topic.

DKK 382.00
3

Oxford Read and Imagine: Level 4:: A Machine for the Future - Paul Shipton - Bog - Oxford University Press - Plusbog.dk

Oxford Read and Imagine: Level 4:: A Machine for the Future - Paul Shipton - Bog - Oxford University Press - Plusbog.dk

Oxford Read and Imagine is a fiction series written for primary and pre-primary students. Young learners follow Rosie, Ben and Grandpa on their adventures and grow up with the characters as they read and learn. Why were Ben, Rosie, and Max beside this dark swamp? Why was an amazing machine sinking in it? Could Ben and Clunk get the machine out fast and take everyone home? And what about those hungry alligators?CEFR A1/A2Word count: 1,353

DKK 159.00
1