Автор: William Herring
Название: Learning Radiology: Recognizing the Basics, 3rd Edition
ISBN: 0323328075 ISBN-13(EAN): 9780323328074
Издательство: Elsevier Science
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Автор: Bishop
Название: Pattern Recognition and Machine Learning
ISBN: 0387310738 ISBN-13(EAN): 9780387310732
Издательство: Springer
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Цена: 6244 р.
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Описание: The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.A forthcoming companion volume will deal with practical aspects of pattern recognition and machine learning, and will include free software implementations of the key algorithms along with example data sets and demonstration programs.Christopher Bishop is Assistant Director at Microsoft Research Cambridge, and also holds a Chair in Computer Science at the University of Edinburgh. He is a Fellow of Darwin College Cambridge, and was recently elected Fellow of the Royal Academy of Engineering. The author's previous textbook "Neural Networks for Pattern Recognition" has been widely adopted.Coming soon:*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)*For instructors, worked solutions to remaining exercises from the Springer web site*Lecture slides to accompany each chapter*Data sets available for download

Автор: Malley
Название: Statistical Learning for Biomedical Data
ISBN: 0521699096 ISBN-13(EAN): 9780521699099
Издательство: Cambridge Academ
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Описание: This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests™, neural nets, support vector machines, nearest neighbors and boosting.

Автор: Kevin Murphy
Название: Machine Learning
ISBN: 0262018020 ISBN-13(EAN): 9780262018029
Издательство: Wiley
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Цена: 6078 р.
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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: Kelleher John D., Macnamee Brian, D`Arcy Aoife
Название: Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies
ISBN: 0262029448 ISBN-13(EAN): 9780262029445
Издательство: Wiley
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Цена: 6260 р.
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Описание:

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: Morrison, K.
Название: Cambridge IGCSE Mathematics. Extended Practice Book
ISBN: 1107672724 ISBN-13(EAN): 9781107672727
Издательство: Cambridge Education
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Цена: 1290 р.
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Описание: A series of titles written to cover the complete Cambridge IGCSE Mathematics (0580) syllabus and endorsed by Cambridge International Examinations.

Автор: Mercer Sarah
Название: Psychology for Language Learning
ISBN: 0230301150 ISBN-13(EAN): 9780230301153
Издательство: Springer
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Цена: 2639 р.
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Описание: Offering a timely snapshot of current theory and research in the field of psychology in foreign language learning, this book is accessible to both specialists and non-specialists. Each chapter focuses on a different psychological construct and provides an overview of current thinking in the area drawing on insights from educational psychology.

Автор: Cook, Vivian
Название: Second language learning and language teaching
ISBN: 0340958766 ISBN-13(EAN): 9780340958766
Издательство: Taylor&Francis
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Цена: 2243 р.
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Описание: Second Language Learning and Language Teaching" remains the essential textbook for all student teachers of modern languages and TESOL as well as applied linguistics.

Автор: Harlen, Wynne
Название: Assessment of learning
ISBN: 1412935199 ISBN-13(EAN): 9781412935197
Издательство: Sage Publications
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Цена: 2925 р.
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Описание: Takes a critical look at how students` achievements are assessed for a range of purposes, from reporting progress to selection and qualification. This work considers the relationship between what is taught, and how, and what and how learning outcomes are assessed.

Автор: Lee
Название: Language for learning mathematics: assessment for learning in practice
ISBN: 0335219896 ISBN-13(EAN): 9780335219896
Издательство: McGraw-Hill
Цена: 5610 р.
Наличие на складе: Нет в наличии.

Описание: This book makes math easier to comprehend for your students. For many of your middle-school and high-school students, expressing mathematical ideas isn't easy. The language of math is often abstract and hard to get a handle on.

Now "Assessment for Learning in the Mathematics Classroom" helps you help your students to express what they already know and increase their understanding. Providing practical strategies for bringing more discourse into lesson plans, this book gives you a powerful way to raise standards in the classroom.

Автор: Young
Название: Discursive Practice in Language Learning and Teaching
ISBN: 1405184442 ISBN-13(EAN): 9781405184441
Издательство: Wiley
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Цена: 2804 р.
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Описание: Examines the theory of discursive practice - the linguistic and socio-cultural characteristics of recurring episodes of face-to-face interaction that have social and cultural significance to a community of speakers. This work explores how participants` abilities in a specific discursive practice may be learned, taught and assessed.

Автор: Tobey Cheryl Rose, Keeley Page D.
Название: Mathematics Formative Assessment: 75 Practical Strategies for Linking Assessment, Instruction, and Learning
ISBN: 1412968127 ISBN-13(EAN): 9781412968126
Издательство: Sage Publications
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Описание: This title follows Page Keeley`s bestselling book on Science Formative Assessment. It identifies and describes practical techniques teachers can use to build a rich repertoire of formative assessment strategies for teaching math.