Bandit Algorithms, Tor Lattimore, Csaba Szepesvari
Автор: Cormen, Thomas H., E Название: Introduction to algorithms 3 ed. ISBN: 0262033844 ISBN-13(EAN): 9780262033848 Издательство: MIT Press Рейтинг: Цена: 27588.00 р. Наличие на складе: Нет в наличии.
Описание: A new edition of the essential text and professional reference, with substantial new material on such topics as vEB trees, multithreaded algorithms, dynamic programming, and edge-base flow.
Автор: Barab?si Название: Network Science ISBN: 1107076269 ISBN-13(EAN): 9781107076266 Издательство: Cambridge Academ Рейтинг: Цена: 7762.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Illustrated throughout in full colour, this pioneering textbook, spanning a wide range of disciplines from physics to the social sciences, is the only book needed for an introduction to network science. In modular format, with clear delineation between undergraduate and graduate material, its unique design is supported by extensive online resources.
Автор: Voulgaris Zacharias Название: Julia for Data Science ISBN: 1634621301 ISBN-13(EAN): 9781634621304 Издательство: Gazelle Book Services Рейтинг: Цена: 6200.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Master how to use the Julia language to solve business critical data science challenges. After covering the importance of Julia to the data science community and several essential data science principles, we start with the basics including how to install Julia and its powerful libraries. Many examples are provided as we illustrate how to leverage each Julia command, dataset, and function. Specialised script packages are introduced and described. Hands-on problems representative of those commonly encountered throughout the data science pipeline are provided, and we guide you in the use of Julia in solving them using published datasets. Many of these scenarios make use of existing packages and built-in functions, as we cover: 1. An overview of the data science pipeline along with an example illustrating the key points, implemented in Julia; 2. Options for Julia IDEs; 3. Programming structures and functions; 4. Engineering tasks, such as importing, cleaning, formatting and storing data, as well as performing data pre-processing; 5. Data visualisation and some simple yet powerful statistics for data exploration purposes; 6. Dimensionality reduction and feature evaluation; 7. Machine learning methods, ranging from unsupervised (different types of clustering) to supervised ones (decision trees, random forests, basic neural networks, regression trees, and Extreme Learning Machines); 8. Graph analysis including pinpointing the connections among the various entities and how they can be mined for useful insights. Each chapter concludes with a series of questions and exercises to reinforce what you learned. The last chapter of the book will guide you in creating a data science application from scratch using Julia.
Автор: Kaushik Kumar and J. Paulo Davim Название: Optimization using evolutionary algorithms and metaheuristics ISBN: 0367260441 ISBN-13(EAN): 9780367260446 Издательство: Taylor&Francis Рейтинг: Цена: 25265.00 р. Наличие на складе: Поставка под заказ.
Описание: This book covers developments and advances of algorithm based optimization techniques These techniques were only used for non-engineering problems. This book applies them to engineering problems.
Описание: This undergraduate textbook is a concise introduction to the basic toolbox of structures that allow efficient organization and retrieval of data, key algorithms for problems on graphs, and generic techniques for modeling, understanding, and solving algorithmic problems.
Автор: Asmussen Название: Stochastic Simulation: Algorithms and Analysis ISBN: 038730679X ISBN-13(EAN): 9780387306797 Издательство: Springer Рейтинг: Цена: 6981.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Sampling-based computational methods have become a fundamental part of the numerical toolset of practitioners and researchers across an enormous number of different applied domains and academic disciplines. This book provides a broad treatment of such sampling-based methods , as well as accompanying mathematical analysis of the convergence properties of the methods discussed . The reach of the ideas is illustrated by discussing a wide range of applications and the models that have found wide usage. The first half of the book focusses on general methods, whereas the second half discusses model-specific algorithms. Given the wide range of examples, exercises and applications students, practitioners and researchers in probability, statistics, operations research, economics, finance, engineering as well as biology and chemistry and physics will find the book of value. Soren Asmussen is Professor of Applied Probability at Aarhus University, Denmark and Peter Glynn is Thomas Ford Professor of Engineering at Stanford University.
Автор: White John Название: Bandit Algorithms for Website Optimization ISBN: 1449341330 ISBN-13(EAN): 9781449341336 Издательство: Wiley Рейтинг: Цена: 2533.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book shows you how to run experiments on your website using A/B testing - and then takes you a huge step further by introducing you to bandit algorithms for website optimization.
Автор: Dorota Glowacka Название: Bandit Algorithms in Information Retrieval ISBN: 1680835742 ISBN-13(EAN): 9781680835748 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 12197.00 р. Наличие на складе: Поставка под заказ.
Описание: Provides an overview of bandit algorithms inspired by various aspects of Information Retrieval (IR), such as click models, online ranker evaluation, personalization or the cold-start problem. Using a survey style, each chapter focuses on a specific IR problem and explains how it was addressed with various bandit approaches.
Автор: Agarwal, Dr Basant, Baka, Benjamin Название: Hands-On Data Structures and Algorithms with Python 2 ed ISBN: 1788995570 ISBN-13(EAN): 9781788995573 Издательство: Неизвестно Рейтинг: Цена: 8091.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data structures help us to organize and align the data in a very efficient way. This book will surely help you to learn important and essential data structures through Python implementation for better understanding of the concepts.
A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
Автор: Slivkins, Aleksandrs Название: Introduction to multi-armed bandits ISBN: 168083620X ISBN-13(EAN): 9781680836202 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 13306.00 р. Наличие на складе: Поставка под заказ.
Описание: Provides a textbook like treatment of multi-armed bandits. The work on multi-armed bandits can be partitioned into a dozen or so directions. Each chapter tackles one line of work, providing a self-contained introduction and pointers for further reading.
Описание: Multi-armed bandit problems pertain to optimal sequential decision making and learning in unknown environments. Since the first bandit problem posed by Thompson in 1933 for the application of clinical trials, bandit problems have enjoyed lasting attention from multiple research communities and have found a wide range of applications across diverse domains. This book covers classic results and recent development on both Bayesian and frequentist bandit problems. We start in Chapter 1 with a brief overview on the history of bandit problems, contrasting the two schools—Bayesian and frequentis —of approaches and highlighting foundational results and key applications. Chapters 2 and 4 cover, respectively, the canonical Bayesian and frequentist bandit models. In Chapters 3 and 5, we discuss major variants of the canonical bandit models that lead to new directions, bring in new techniques, and broaden the applications of this classical problem. In Chapter 6, we present several representative application examples in communication networks and social-economic systems, aiming to illuminate the connections between the Bayesian and the frequentist formulations of bandit problems and how structural results pertaining to one may be leveraged to obtain solutions under the other.
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