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Machine Learning for Neuroscience A Systematic Approach

Machine Learning for Neuroscience A Systematic Approach

This book addresses the growing need for machine learning and data mining in neuroscience. The book offers a basic overview of the neuroscience machine learning and the required math and programming necessary to develop reliable working models. The material is presented in a easy to follow user-friendly manner and is replete with fully working machine learning code. Machine Learning for Neuroscience: A Systematic Approach tackles the needs of neuroscience researchers and practitioners that have very little training relevant to machine learning. The first section of the book provides an overview of necessary topics in order to delve into machine learning including basic linear algebra and Python programming. The second section provides an overview of neuroscience and is directed to the computer science oriented readers. The section covers neuroanatomy and physiology cellular neuroscience neurological disorders and computational neuroscience. The third section of the book then delves into how to apply machine learning and data mining to neuroscience and provides coverage of artificial neural networks (ANN) clustering and anomaly detection. The book contains fully working code examples with downloadable working code. It also contains lab assignments and quizzes making it appropriate for use as a textbook. The primary audience is neuroscience researchers who need to delve into machine learning programmers assigned neuroscience related machine learning projects and students studying methods in computational neuroscience. | Machine Learning for Neuroscience A Systematic Approach

GBP 82.99
1

Statistical Machine Learning A Unified Framework

Statistical Machine Learning A Unified Framework

The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing analyzing evaluating and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students engineers and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular the material in this text directly supports the mathematical analysis and design of old new and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised unsupervised and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive batch minibatch MCEM and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics computer science electrical engineering and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students professional engineers and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph. D. M. S. E. E. B. S. E. E. ) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models. | Statistical Machine Learning A Unified Framework

GBP 99.99
1

Marketing Analytics A Machine Learning Approach

Marketing Analytics A Machine Learning Approach

With businesses becoming ever more competitive marketing strategies need to be more precise and performance oriented. Companies are investing considerably in analytical infrastructure for marketing. This new volume Marketing Analytics: A Machine Learning Approach enlightens readers on the application of analytics in marketing and the process of analytics providing a foundation on the concepts and algorithms of machine learning and statistics. The book simplifies analytics for businesses and explains its uses in different aspects of marketing in a way that even marketers with no prior analytics experience will find it easy to follow giving them to tools to make better business decisions. This volume gives a comprehensive overview of marketing analytics incorporating machine learning methods of data analysis that automates analytical model building. The volume covers the important aspects of marketing analytics including segmentation and targeting analysis statistics for marketing marketing metrics consumer buying behavior neuromarketing techniques for consumer analytics new product development forecasting sales and price web and social media analytics and much more. This well-organized and straight-forward volume will be valuable for marketers managers decision makers and research scholars and faculty in business marketing and information technology and would also be suitable for classroom use. | Marketing Analytics A Machine Learning Approach

GBP 124.00
1

The Writing Machine A History of the Typewriter

A First Course in Machine Learning

A First Course in Machine Learning

A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings and goes all the way to the frontiers of the subject such as infinite mixture models GPs and MCMC. —Devdatt Dubhashi Professor Department of Computer Science and Engineering Chalmers University Sweden This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade. —Daniel Barbara George Mason University Fairfax Virginia USA The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling inference and prediction providing ‘just in time’ the essential background on linear algebra calculus and probability theory that the reader needs to understand these concepts. —Daniel Ortiz-Arroyo Associate Professor Aalborg University Esbjerg Denmark I was impressed by how closely the material aligns with the needs of an introductory course on machine learning which is its greatest strength…Overall this is a pragmatic and helpful book which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months. —David Clifton University of Oxford UK The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process MCMC and mixture modeling provide an ideal basis for practical projects without disturbing the very clear and readable exposition of the basics contained in the first part of the book. —Gavin Cawley Senior Lecturer School of Computing Sciences University of East Anglia UK This book could be used for junior/senior undergraduate students or first-year graduate students as well as individuals who want to explore the field of machine learning…The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective. —Guangzhi Qu Oakland University Rochester Michigan USA

GBP 39.99
1

The Handbook of Human-Machine Interaction A Human-Centered Design Approach

ActiveCare Washing Machine NM11 1045 WC A

ActiveCare Washing Machine NM11 1045 WC A

For an exceptional stain-busting, intuitive laundry solution that protects and cares for your clothes, the Hotpoint ActiveCare NM11 1045 WC A Washing Machine delivers an incredible A+++ (-20%) energy rating, 10kg capacity, 1400rpm spin speed and wealth of programs to simplify your life. Best-in-class for stain removal, our innovative ActiveCare technology takes care of your clothes by removing more than 100 stains at just 20C, so you can wear your favourite clothes time and time again. Maintain hygiene and freshness in the most natural way with the steam pack. The steam hygiene and steam refresh cycles provide the ultimate care and protection for keeping your laundry fresh and perfectly clean. Steam refresh provides a dedicated cycle to freshen clothes without the need for a full wash. The cycle takes just 20 minutes with steam penetrating deep into fabrics, making them slightly damp, reducing creases for easier ironing. The steam hygiene cycle injects steam directly into the drum at the end of washing cycle, removing up to 99.9% of the most common bacteria without the use of chemical additives, always respecting the environment. Dropped an item on the way to the washing machine? With Hotpoint Stop & Add you can add laundry to the wash cycle during the first few minutes of the cycle without compromising on wash performance. You can also choose either to speed up the cycle or save energy thanks to the Rapid Eco option. With the Rapid option, the wash duration is reduced by up to 50%, without compromising on washing results. With the Eco option, the washing machine reduces energy consumption by up to 20%, making it gentle on your pocket. Keep your laundry soft and help to reduce creasing with Final Care, this option will activate a special tumbling action for up to 6 hours after the end of the cycle. Easy operated through the large LED display, the Hotpoint ActiveCare NM11 1045 WC A Washing Machine gives you more time back for the things you love.

GBP 0.00
1

Human-Machine Interaction and IoT Applications for a Smarter World

GBP 130.00
1

Machine Learning for Managers

Dettol Washing Machine Cleaner 250ml