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Mathematics for Machine Learning, Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong


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Цена: 6334.00р.
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Автор: Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
Название:  Mathematics for Machine Learning
Перевод названия: Марк Питер Дайзенрот, А. Альдо Фейсал, Чен Сун Он: Математика для машинного обучения
ISBN: 9781108455145
Издательство: Cambridge Academ
Классификация:




ISBN-10: 110845514X
Обложка/Формат: Paperback
Страницы: 398
Вес: 0.77 кг.
Дата издания: 31.03.2020
Серия: Mathematics
Язык: English
Иллюстрации: Worked examples or exercises; 106 halftones, color; 3 halftones, black and white
Размер: 17.78 x 1.78 x 25.15 cm
Читательская аудитория: Tertiary education (us: college)
Ключевые слова: Machine learning,Pattern recognition,Probability & statistics,Maths for engineers, COMPUTERS / Computer Vision & Pattern Recognition
Ссылка на Издательство: Link
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Поставляется из: Англии
Описание: This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.


Pattern Recognition and Machine Learning

Автор: Christopher M. Bishop
Название: Pattern Recognition and Machine Learning
ISBN: 0387310738 ISBN-13(EAN): 9780387310732
Издательство: Springer
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Цена: 11878.00 р.
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Описание: Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Dynamical Systems: Stability, Symbolic Dynamics, and Chaos ( Studies in Advanced Mathematics #28 )

Автор: Robinson, Clark
Название: Dynamical Systems: Stability, Symbolic Dynamics, and Chaos ( Studies in Advanced Mathematics #28 )
ISBN: 0849384958 ISBN-13(EAN): 9780849384950
Издательство: Taylor&Francis
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Цена: 29093.00 р.
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Описание: Treats the dynamics of both iteration of functions and solutions of ordinary differential equations. This book introduces various concepts for iteration of functions where the geometry is simpler, but results are interpreted for differential equations. It concentrates on properties of the whole system or subsets of the system.

Matrix Differential Calculus with Applications in Statistics and Econometrics

Автор: Jan R. Magnus, Heinz Neudecker
Название: Matrix Differential Calculus with Applications in Statistics and Econometrics
ISBN: 1119541204 ISBN-13(EAN): 9781119541202
Издательство: Wiley
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Цена: 14090.00 р.
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Описание:

A brand new, fully updated edition of a popular classic on matrix differential calculus with applications in statistics and econometrics

This exhaustive, self-contained book on matrix theory and matrix differential calculus provides a treatment of matrix calculus based on differentials and shows how easy it is to use this theory once you have mastered the technique. Jan Magnus, who, along with the late Heinz Neudecker, pioneered the theory, develops it further in this new edition and provides many examples along the way to support it.

Matrix calculus has become an essential tool for quantitative methods in a large number of applications, ranging from social and behavioral sciences to econometrics. It is still relevant and used today in a wide range of subjects such as the biosciences and psychology. Matrix Differential Calculus with Applications in Statistics and Econometrics, Third Edition contains all of the essentials of multivariable calculus with an emphasis on the use of differentials. It starts by presenting a concise, yet thorough overview of matrix algebra, then goes on to develop the theory of differentials. The rest of the text combines the theory and application of matrix differential calculus, providing the practitioner and researcher with both a quick review and a detailed reference.

  • Fulfills the need for an updated and unified treatment of matrix differential calculus
  • Contains many new examples and exercises based on questions asked of the author over the years
  • Covers new developments in field and features new applications
  • Written by a leading expert and pioneer of the theory
  • Part of the Wiley Series in Probability and Statistics

Matrix Differential Calculus With Applications in Statistics and Econometrics Third Edition is an ideal text for graduate students and academics studying the subject, as well as for postgraduates and specialists working in biosciences and psychology.

Mathematics for Finance

Автор: Capinski
Название: Mathematics for Finance
ISBN: 0857290819 ISBN-13(EAN): 9780857290816
Издательство: Springer
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Цена: 4884.00 р.
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Описание: Mathematics for Finance: An Introduction to Financial Engineering combines financial motivation with mathematical style.

Introduction to Mathematical Portfolio Theory

Автор: Joshi
Название: Introduction to Mathematical Portfolio Theory
ISBN: 1107042313 ISBN-13(EAN): 9781107042315
Издательство: Cambridge Academ
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Цена: 9029.00 р.
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Описание: A concise yet comprehensive guide to the mathematics of portfolio theory from a modelling perspective, with discussion of the assumptions, limitations and implementations of the models as well as the theory underlying them. Aimed at advanced undergraduates, this book can be used for self-study or as a course text.

Statistical Analysis with Missing Data, Third Edit ion

Автор: Little
Название: Statistical Analysis with Missing Data, Third Edit ion
ISBN: 0470526793 ISBN-13(EAN): 9780470526798
Издательство: Wiley
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Цена: 12664.00 р.
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Описание: Reflecting new application topics, Statistical Analysis with Missing Data offers a thoroughly up-to-date, reorganized survey of current methodology for handling missing data problems. The third edition reviews historical approaches to the subject and describe rigorous yet simple methods for multivariate analysis with missing values.

Nonlinear Dynamics and Chaos

Автор: Strogatz Steven
Название: Nonlinear Dynamics and Chaos
ISBN: 0813349109 ISBN-13(EAN): 9780813349107
Издательство: Taylor&Francis
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Цена: 10564.00 р.
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Описание: This textbook is aimed at newcomers to nonlinear dynamics and chaos, especially students taking a first course in the subject. The presentation stresses analytical methods, concrete examples, and geometric intuition. The theory is developed systematically, starting with first-order differential equations and their bifurcations, followed by phase plane analysis, limit cycles and their bifurcations, and culminating with the Lorenz equations, chaos, iterated maps, period doubling, renormalization, fractals, and strange attractors.A unique feature of the book is its emphasis on applications. These include mechanical vibrations, lasers, biological rhythms, superconducting circuits, insect outbreaks, chemical oscillators, genetic control systems, chaotic waterwheels, and even a technique for using chaos to send secret messages. In each case, the scientific background is explained at an elementary level and closely integrated with mathematical theory.In the twenty years since the first edition of this book appeared, the ideas and techniques of nonlinear dynamics and chaos have found application to such exciting new fields as systems biology, evolutionary game theory, and sociophysics. This second edition includes new exercises on these cutting-edge developments, on topics as varied as the curiosities of visual perception and the tumultuous love dynamics in Gone With the Wind .

Introduction to High-Dimensional Statistics

Автор: Giraud
Название: Introduction to High-Dimensional Statistics
ISBN: 1482237946 ISBN-13(EAN): 9781482237948
Издательство: Taylor&Francis
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Цена: 9645.00 р.
Наличие на складе: Поставка под заказ.

Описание: Ever-greater computing technologies have given rise to an exponentially growing volume of data. Today massive data sets (with potentially thousands of variables) play an important role in almost every branch of modern human activity, including networks, finance, and genetics. However, analyzing such data has presented a challenge for statisticians and data analysts and has required the development of new statistical methods capable of separating the signal from the noise. Introduction to High-Dimensional Statistics is a concise guide to state-of-the-art models, techniques, and approaches for handling high-dimensional data. The book is intended to expose the reader to the key concepts and ideas in the most simple settings possible while avoiding unnecessary technicalities. Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this highly accessible text: Describes the challenges related to the analysis of high-dimensional data Covers cutting-edge statistical methods including model selection, sparsity and the lasso, aggregation, and learning theory Provides detailed exercises at the end of every chapter with collaborative solutions on a wikisite Illustrates concepts with simple but clear practical examples Introduction to High-Dimensional Statistics is suitable for graduate students and researchers interested in discovering modern statistics for massive data. It can be used as a graduate text or for self-study.

Statistical Learning with Sparsity

Автор: Hastie
Название: Statistical Learning with Sparsity
ISBN: 1498712169 ISBN-13(EAN): 9781498712163
Издательство: Taylor&Francis
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Цена: 16843.00 р.
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Описание:

Discover New Methods for Dealing with High-Dimensional Data

A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data.

Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of 1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso.

In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.

Python machine learning -

Автор: Raschka, Sebastian Mirjalili, Vahid
Название: Python machine learning -
ISBN: 1787125939 ISBN-13(EAN): 9781787125933
Издательство: Неизвестно
Цена: 8091.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This second edition of Python Machine Learning by Sebastian Raschka is for developers and data scientists looking for a practical approach to machine learning and deep learning. In this updated edition, you`ll explore the machine learning process using Python and the latest open source technologies, including scikit-learn and TensorFlow 1.x.

Actuarial Mathematics for Life Contingent Risks

Автор: Dickson, David C. M.
Название: Actuarial Mathematics for Life Contingent Risks
ISBN: 1107044073 ISBN-13(EAN): 9781107044074
Издательство: Cambridge Academ
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Цена: 12514.00 р.
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Описание: Actuarial Mathematics for Life Contingent Risks, 2nd edition, is the sole required text for the Society of Actuaries Exam MLC Fall 2015 and Spring 2016. It covers the entire syllabus for the SOA Exam MLC, including new sections for Spring 2016. It is ideal for university courses and for individuals preparing for professional actuarial examinations - especially the new, long-answer exam questions. Three leaders in actuarial science balance rigor with intuition and emphasize practical applications using computational techniques to provide a modern perspective on life contingencies and equip students for the products and risk structures of the future. The authors then develop a more contemporary outlook, introducing multiple state models, emerging cash flows and embedded options. The 210 exercises provide meaningful practice with both long-answer and multiple choice questions. Furthermore: • the book has been updated to include new material on discrete time Markov processes, on models involving joint lives, and on universal life insurance and participating traditional insurance • the Solutions Manual (ISBN 9781107620261), available for separate purchase, provides detailed solutions to the text's exercises.

An Introduction to Machine Learning

Автор: Miroslav Kubat
Название: An Introduction to Machine Learning
ISBN: 3319348868 ISBN-13(EAN): 9783319348865
Издательство: Springer
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Цена: 6986.00 р.
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Описание: This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications.


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