Advances in Financial Machine Learning, Marcos Lopez de Prado
Автор: Kevin Murphy Название: Machine Learning ISBN: 0262018020 ISBN-13(EAN): 9780262018029 Издательство: MIT Press Рейтинг: Цена: 18622.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
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.
Автор: Marsland Название: Machine Learning ISBN: 1466583282 ISBN-13(EAN): 9781466583283 Издательство: Taylor&Francis Рейтинг: Цена: 12095.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
A Proven, Hands-On Approach for Students without a Strong Statistical Foundation
Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area.
Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.
New to the Second Edition
Two new chapters on deep belief networks and Gaussian processes
Reorganization of the chapters to make a more natural flow of content
Revision of the support vector machine material, including a simple implementation for experiments
New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron
Additional discussions of the Kalman and particle filters
Improved code, including better use of naming conventions in Python
Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author's website.
Автор: Barber Название: Bayesian Reasoning and Machine Learning ISBN: 0521518148 ISBN-13(EAN): 9780521518147 Издательство: Cambridge Academ Рейтинг: Цена: 11088.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors.
Автор: Clarke Название: Principles and Theory for Data Mining and Machine Learning ISBN: 0387981349 ISBN-13(EAN): 9780387981345 Издательство: Springer Рейтинг: Цена: 27950.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Extensive treatment of the most up-to-date topicsProvides the theory and concepts behind popular and emerging methodsRange of topics drawn from Statistics, Computer Science, and Electrical Engineering
Автор: Conway Drew, White John Myles Название: Machine Learning for Hackers ISBN: 1449303714 ISBN-13(EAN): 9781449303716 Издательство: Wiley Рейтинг: Цена: 6334.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Now that storage and collection technologies are cheaper and more precise, methods for extracting relevant information from large datasets is within the reach any experienced programmer willing to crunch data.
Автор: David Hillier,Mark Grinblatt Название: Financial Markets and Corporate Strategy 2 ed. ISBN: 0077129423 ISBN-13(EAN): 9780077129422 Издательство: McGraw-Hill Рейтинг: Цена: 10637.00 р. Наличие на складе: Поставка под заказ.
Описание: Financial Markets and Corporate Strategy
Автор: Jong, Frank De Rindi, Barbara Название: Microstructure of financial markets ISBN: 0521687276 ISBN-13(EAN): 9780521687270 Издательство: Cambridge Academ Рейтинг: Цена: 6019.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The first graduate level textbook to cover the theory and empirics of the emerging sub-discipline of financial market microstructure. With numerous end-of-chapter exercises and a companion website, the book is ideally suited for students taking graduate courses in finance as well as being a useful reference for practitioners.
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: Shalev-Shwartz Название: Understanding Machine Learning ISBN: 1107057132 ISBN-13(EAN): 9781107057135 Издательство: Cambridge Academ Рейтинг: Цена: 11194.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. This book explains the principles behind the automated learning approach and the considerations underlying its usage. The authors explain the `hows` and `whys` of machine-learning algorithms, making the field accessible to both students and practitioners.
Автор: Mitchell Название: Machine Learning ISBN: 0071154671 ISBN-13(EAN): 9780071154673 Издательство: McGraw-Hill Рейтинг: Цена: 10466.00 р. Наличие на складе: Поставка под заказ.
Описание: Covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. This book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.
Автор: Jacek Koronacki; Zbigniew W. Ras; Slawomir T. Wier Название: Advances in Machine Learning II ISBN: 3642051782 ISBN-13(EAN): 9783642051784 Издательство: Springer Рейтинг: Цена: 36197.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: General Issues.- Knowledge-Oriented and Distributed Unsupervised Learning for Concept Elicitation.- Toward Interactive Computations: A Rough-Granular Approach.- Data Privacy: From Technology to Economics.- Adapting to Human Gamers Using Coevolution.- Wisdom of Crowds in the Prisoner's Dilemma Context.- Logical and Relational Learning, and Beyond.- Towards Multistrategic Statistical Relational Learning.- About Knowledge and Inference in Logical and Relational Learning.- Two Examples of Computational Creativity: ILP Multiple Predicate Synthesis and the 'Assets' in Theorem Proving.- Logical Aspects of the Measures of Interestingness of Association Rules.- Text and Web Mining.- Clustering the Web 2.0.- Induction in Multi-Label Text Classification Domains.- Cluster-Lift Method for Mapping Research Activities over a Concept Tree.- On Concise Representations of Frequent Patterns Admitting Negation.- Classification and Beyond.- A System to Detect Inconsistencies between a Domain Expert's Different Perspectives on (Classification) Tasks.- The Dynamics of Multiagent Q-Learning in Commodity Market Resource Allocation.- Simple Algorithms for Frequent Item Set Mining.- Monte Carlo Feature Selection and Interdependency Discovery in Supervised Classification.- Machine Learning Methods in Automatic Image Annotation.- Neural Networks and Other Nature Inspired Approaches.- Integrative Probabilistic Evolving Spiking Neural Networks Utilising Quantum Inspired Evolutionary Algorithm: A Computational Framework.- Machine Learning in Vector Models of Neural Networks.- Nature Inspired Multi-Swarm Heuristics for Multi-Knowledge Extraction.- Discovering Data Structures Using Meta-learning, Visualization and Constructive Neural Networks.- Neural Network and Artificial Immune Systems for Malware and Network Intrusion Detection.- Immunocomputing for Speaker Recognition.
Описание: Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining?including both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. <br><br>Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. <br><br>The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; new chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material.<br> <br><br>* Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques<br><br>* Algorithmic methods at the heart of successful data mining?including tired and true methods as well as leading edge methods<br><br>* Performance improvement techniques that work by transforming the input or output<br><br>* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization?in an updated, interactive interface. <br>
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