Search Results

ENGLISH

Machine Learning For Data Streams - With Practical Examples In Moa

by Albert Bifet, Bernhard Pfahringer, Geoffrey Holmes e Ricard Gavalda

3,96€ + 10% CARD

 A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework.
ENGLISH

Machine Learning - A Probabilistic Perspective

by Kevin P. Murphy

331,25€ + 10% CARD

 A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.Today''s Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
ENGLISH

Causation, Prediction, And Search

by Clark Glymour, Peter Spirtes e Richard Scheines

139,13€ + 10% CARD

 This text addresses the questions of causal knowledge, planning and prediction in computer science. The authors use the formalism of the Bayes networks to achieve research results in the social, behavioural and physical sciences.
ENGLISH

Probabilistic Graphical Models - Principles And Techniques

by Daphne Koller e Nir Friedman

357,75€ + 10% CARD

 A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
ENGLISH

Graphical Models For Machine Learning And Digital Communication

by Brendan J. Frey

74,73€ + 10% CARD

 In this text, Brendan Frey uses graphical models as an overarching framework to describe and solve problems of pattern classification, unsupervised learning, data compression, and channel coding.
ENGLISH

Reinforcement Learning - An Introduction

by Andrew G. Barto e Richard S. Sutton

149,73€ + 10% CARD

 An account of key ideas and algorithms in reinforcement learning. The discussion ranges from the history of the field's intellectual foundations to recent developments and applications. Areas studied include reinforcement learning problems in terms of Markov decision problems and solution methods.
ENGLISH

Learning With Kernels - Support Vector Machines, Regularization, Optimization, And Beyond - Support Vector Machines, Regularization, Optimization, And Beyond

by Alexander J. Smola

139,92€ + 10% CARD

 A comprehensive introduction to Support Vector Machines and related kernel methods.
ENGLISH

Principles Of Data Mining

by David J. Hand, Heikki Mannila e Padhraic Smyth

169,60€ + 10% CARD

 The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
ENGLISH

Semi-Supervised Learning

119,25€ + 10% CARD

 A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research.
ENGLISH

Machine Learning In Non-Stationary Environments - Introduction To Covariate Shift Adaptation

by Masashi Sugiyama e Motoaki Kawanabe

145,75€ + 10% CARD

 Theory, algorithms, and applications of machine learning techniques to overcome "covariate shift" non-stationarity.
ENGLISH

Gaussian Processes For Machine Learning

by Carl Edward Rasmussen e Christopher K. I. Williams

145,75€ + 10% CARD

 Presents a comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. This book deals with the supervised-learning problem for both regression and classification, and includes algorithms. It also contains illustrative examples and exercises.
ENGLISH

The Minimum Description Length Principle

by Peter D. (Centrum Voor Wiskunde En Informatica) Grunwald

106,53€ + 10% CARD

 A comprehensive introduction and reference guide to the minimum description length (MDL) Principle that is useful for researchers dealing with inductive reference in diverse areas including statistics, pattern classification, machine learning, data mining, biology, econometrics, and experimental psychology.
ENGLISH

Introduction To Statistical Relational Learning

120,84€ + 10% CARD

 Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications.
ENGLISH

Boosting - Foundations And Algorithms - Foundations And Algorithms

by Robert E. (Principal Researcher, Microsoft Research) Schapire e Yoav (Professor, University Of California, San Diego) Freund

66,78€ + 10% CARD

 An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones.Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical.This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well.The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
ENGLISH

Learning Kernel Classifiers - Theory And Algorithms - Theory And Algorithms

by Ralf Herbrich

92,22€ + 10% CARD

 An overview of the theory and application of kernel classification methods.
ENGLISH

Gaussian Processes For Machine Learning

by Carl Edward (University Of Cambridge) Rasmussen e Christopher K. I. (University Of Edinburgh) Williams

70,30€ + 10% CARD

free shipping

 Presents a comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. This book deals with the supervised-learning problem for both regression and classification, and includes algorithms. It also contains illustrative examples and exercises.
ENGLISH

The Abundant University - Remaking Higher Education For A Digital World

by Michael D. Smith

51,37€ + 10% CARD

free shipping
ENGLISH

Very Fantastically Arranged

by Caragh Thuring

74,35€ + 10% CARD

free shipping
ENGLISH

We Are All Monsters - How Deviant Organisms Came To Define Us

by Andrew Mangham

40,56€ + 10% CARD

free shipping
ENGLISH

Ecological By Design - A History From Scandinavia

by Kjetil Fallan

51,37€ + 10% CARD

free shipping
ENGLISH

Selfie Democracy - The New Digital Politics Of Disruption And Insurrection

by Elizabeth Losh

33,80€ + 10% CARD

free shipping
ENGLISH

Introduction To Online Convex Optimization, Second Edition

by Elad Hazan

77,06€ + 10% CARD

free shipping
ENGLISH

Introduction To Machine Learning

by Ethem (Ozyegin University) Alpaydin

114,91€ + 10% CARD

free shipping
ENGLISH

Introduction To Natural Language Processing

by Jacob (Assistant Professor, Google) Eisenstein

97,34€ + 10% CARD

free shipping
Loading results...

View more results