Empfehlungen basierend auf "Foundations of Machine Learning"

Acclaimed one of the "seminal books... comparable in ultimate importance to... Galileo or Malthus or Rousseau or Mill," Cybernetics was judged by twenty-seven historians, economists, educators, and philosophers to be one of those books published during the "past four decades," which may have a substantial impact on public thought and action in the years ahead.—Saturday Review

How can we objectively define categories of truth in scientific thinking? How can we reliably measure the results of research? In this ground-breaking text, Dienes undertakes a comprehensive historical analysis of the dominant schools of thought, key theories and influential thinkers that have progressed the foundational principles and characteristics that typify scientific research methodology today. This book delivers a masterfully simple, 'though not simplistic', introduction to the core arguments surrounding Popper, Kuhn and Lakatos, Fisher and Royall, Neyman and Pearson and Bayes. Subsequently, this book clarifies the prevalent misconceptions that surround such theoretical perspectives in psychology today, providing an especially accessible critique for student readers.This book launches an informative inquiry into the methods by which psychologists throughout history have arrived at the conclusions of research, equipping readers with the knowledge to accurately design and evaluate their own research and gain confidence in critiquing results in psychology research. Particular attention is given to understanding methods of measuring the falsifiability of statements, probabilities and the differing views on statistical inference. An illuminating book for any undergraduate psychology student taking courses in critical thinking, research methods, BPS's core area 'conceptual and historical issues' as well as those studying masters, phd's and experienced researchers.

The third edition of the book widely recognized as providing the most readable and clearly articulated introduction to Cognitive Linguistics is fully revised and updated to include the considerable developments in Cognitive Linguistics since 1987. It covers recent research on polysemy, meaning relatedness and metaphors, as well as expanding the discussion of syntactic categories and the relevance of computer simulations.

"Naval architecture was born in the mountains of Peru, in the mind of a French astronomer named Pierre Bouguer who never built a ship in his life." So writes Larrie Ferreiro at the beginning of this pioneering work on the science of naval architecture. Bouguer's monumental book Traité du navire (Treatise of the Ship) founded a discipline that defined not the rules for building a ship but the theories and tools to predict a ship's characteristics and performance before it was built. InShips and Science, Ferreiro argues that the birth of naval architecture formed an integral part of the Scientific Revolution. Using Bouguer's work as a cornerstone, Ferreiro traces the intriguing and often unexpected development of this new discipline and describes its practical application to ship design in the seventeenth and eighteenth centuries. Drawing on previously untapped primary-source and archival information, he places the development of naval architecture in the contexts of science, navy, and society, across the major shipbuilding nations of Britain, France, Spain, the Netherlands, Sweden, Denmark, and Italy.Ferreiro describes the formulation of the three major elements of ship theory (the science of explaining the physical behavior of a ship): maneuvering and sail theory, ship resistance and hydrodynamics, and stability theory. He considers the era's influential books on naval architecture and describes the professionalization of ship constructors that is the true legacy of this period. Finally, looking from the viewpoints of both the constructor and the naval administrator, he explains why the development of ship theory was encouraged, financed, and used in naval shipbuilding. A generous selection of rarely seen archival images accompanies the text.

This edition of Robert Sedgewick and Kevin Wayne’s Algorithms is one of the most popular textbooks on algorithms today and is widely used in colleges and universities worldwide. The algorithms in this book – including 50 algorithms every programmer should know – represent a body of knowledge developed over the last 50 years that has become indispensable, not just for professional programmers and computer science students but for any student with interests in science, mathematics, and engineering and for students who use computation in the liberal arts. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use.

Global Physical Climatology is an introductory text devoted to the fundamental physical principles and problems of climate sensitivity and change. Addressing some of the most critical issues in climatology, this text features incisive coverage of topics that are central to understanding orbital parameter theory for past climate changes, and for anthropogenic and natural causes of near-future changes--Key Features* Covers the physics of climate change* Examines the nature of the current climate and its previous changes* Explores the sensitivity of climate and the mechanisms by which humans are likely to produce near-future climate changes* Provides instructive end-of-chapter exercises and appendices

An Introduction to Dynamic Meteorology, Fourth Edition presents a cogent explanation of the fundamentals of meteorology, and explains storm dynamics for weather-oriented meteorologists. This revised edition features updated treatments on climate dynamics, tropical meteorology, middle atmosphere dynamics, and numerical prediction. It contains a wealth of illustrations to elucidate text and equations, plus end-of-chapter problems. This book is recommended for senior and graduate students in meteorology and atmospheric science, as well as atmospheric scientists desiring a broad overview of dynamical meteorology. Provides clear physical explanations of key dynamical principles Contains a wealth of illustrations to elucidate text and equations, plus end-of-chapter problems Holton is one of the leading authorities in contemporary meteorology, and well known for his clear writing style NEW IN THIS EDITION Updated treatments on climate dynamics, tropical meteorology, middle atmosphere dynamics, and numerical prediction

Discover how planes get--and stay--airborne Now you can truly master an understanding of the phenomenon of flight. This practical guide is the most intuitive introduction to basic flight mechanics available. Understanding Flight, Second Edition, explains the principles of aeronautics in terms, descriptions, and illustrations that make sense--without complicated mathematics. Updated to include helicopter flight fundamentals and aircraft structures, this aviation classic is required reading for new pilots, students, engineers, and anyone fascinated with flight. Understanding Flight, Second Edition, covers: Physics of flight Wing design and configuration Stability and control Propulsion High-speed flight Performance and safety Aerodynamic testing Helicopters and autogyros Aircraft structures and materials  

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.Olivier Chapelle and Alexander Zien are Research Scientists and Bernhard Schölkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in Tübingen. Schölkopf is coauthor of Learning with Kernels (MIT Press, 2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by The MIT Press.

The minimum description length (MDL) principle is a powerful method of inductive inference, the basis of statistical modeling, pattern recognition, and machine learning. It holds that the best explanation, given a limited set of observed data, is the one that permits the greatest compression of the data. MDL methods are particularly well-suited for dealing with model selection, prediction, and estimation problems in situations where the models under consideration can be arbitrarily complex, and overfitting the data is a serious concern.This extensive, step-by-step introduction to the MDL Principle provides a comprehensive reference (with an emphasis on conceptual issues) that is accessible to graduate students and researchers in statistics, pattern classification, machine learning, and data mining, to philosophers interested in the foundations of statistics, and to researchers in other applied sciences that involve model selection, including biology, econometrics, and experimental psychology. Part I provides a basic introduction to MDL and an overview of the concepts in statistics and information theory needed to understand MDL. Part II treats universal coding, the information-theoretic notion on which MDL is built, and part III gives a formal treatment of MDL theory as a theory of inductive inference based on universal coding. Part IV provides a comprehensive overview of the statistical theory of exponential families with an emphasis on their information-theoretic properties. The text includes a number of summaries, paragraphs offering the reader a "fast track" through the material, and boxes highlighting the most important concepts.