Elements of Causal Inference: Foundations and Learning Algorithms (Peters Jonas)(Pevná vazba)

Elements of Causal Inference: Foundations and Learning Algorithms (Peters Jonas)(Pevná vazba)

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a

Information Theory, Inference and Learning Algorithms (MacKay David J. C.)(Pevná vazba)

Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational

Causal Inference in Statistics: A Primer (Pearl Judea)(Paperback)

CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause-effect relationships, we cannot use data to answer questions as basic as 'Does this treatment harm or help patients?' But though hundreds of introductory texts are

Machine Learning Refined: Foundations, Algorithms, and Applications (Watt Jeremy)(Pevná vazba)

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the

Explanation in Causal Inference: Methods for Mediation and Interaction (Vanderweele Tyler)(Pevná vazba)

The book provides an accessible but comprehensive overview of methods for mediation and interaction. There has been considerable and rapid methodological development on mediation and moderation/interaction analysis within the causal-inference literature over the last ten years. Much of this

Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction (Imbens Guido W.)(Pevná vazba)

Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with

Data Mining and Machine Learning: Fundamental Concepts and Algorithms (Zaki Mohammed J.)(Pevná vazba)

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and

Image Texture Analysis: Foundations, Models and Algorithms (Hung Chih-Cheng)(Pevná vazba)

Part I: Existing Models and Algorithms for Image Texture Image Texture, Texture Features, and Image Texture Classification and Segmentation Texture Features and Image Texture Models Algorithms for Image Texture Classification Dimensionality Reduction and Sparse Representation Part II: The K-Views

Computer Vision (Prince Simon J. D.)(Pevná vazba)

This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the

Foundations of Machine Learning, Second Edition (Mohri Mehryar)(Pevná vazba)

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in

Linear Algebra and Learning from Data (Strang Gilbert)(Pevná vazba)

Linear algebra and the foundations of deep learning, together at last From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This

The Machine Learning: A Probabilistic Perspective (Murphy Kevin P.)(Pevná vazba)

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

Foundations of Analytical Chemistry: A Teaching-Learning Approach (Valcrcel Cases Miguel)(Pevná vazba)

Bandit Algorithms (Lattimore Tor)(Pevná vazba)

Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including

Theoretical Foundations of Learning Environments(Paperback / softback)

Theoretical Foundations of Learning Environments provides students, faculty, and instructional designers with a clear, concise introduction to the major pedagogical and psychological theories and their implications for the design of new learning environments for schools, universities, or

Foundations of Astrophysics (Ryden Barbara)(Pevná vazba)

Foundations of Astrophysics provides a contemporary and complete introduction to astrophysics for astronomy and physics majors. With a logical presentation and conceptual and quantitative end-of-chapter problems, the material is accessible to introductory astrophysics students taking a two-semester

Video in the Age of Digital Learning (Kster Jonas)(Pevná vazba)

Chapter 1: Video for LearningLearning in the Digital AgeThe Complexity of the Digital Learning ExperienceLearner-Centered ApproachThe Learner Within the Digital Learning EcosystemThe InstitutionThe InstructorThe Physical Learning SpaceDigital Learning MaterialsLearning TechnologiesLearning

Foundations of Affective Social Learning: Conceptualizing the Social Transmission of Value (Dukes Daniel)(Pevná vazba)

Written by experts in comparative, developmental, social, cognitive and cultural psychology, this book introduces the novel concept of affective social learning to help explain why what matters to us, matters to us. In the same way that social learning describes how we observe other people's

Foundations of Data Science (Blum Avrim)(Pevná vazba)

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques

The Last Testament (Bendiksen Jonas)(Pevná vazba)

Imagined as a sequel to the Old and New Testaments of the Bible by Magnum photographer Jonas Bendiksen, The Last Testament features visual accounts and stories of seven men around the world who claim to be the Second Coming of Jesus Christ. Building on biblical form and structure, chapters

A Mark of the Mental: In Defense of Informational Teleosemantics (Neander Karen)(Pevná vazba)

Drawing on insights from causal theories of reference, teleosemantics, and state space semantics, a theory of naturalized mental representation.In A Mark of the Mental, Karen Neander considers the representational power of mental states--described by the cognitive scientist Zenon Pylyshyn as the

Beyond the Worst-Case Analysis of Algorithms (Roughgarden Tim)(Pevná vazba)

Understanding when and why algorithms work is a fundamental challenge. For problems ranging from clustering to linear programming to neural networks there are significant gaps between empirical performance and prediction based on traditional worst-case analysis. The book introduces exciting new

Distributed Algorithms, Second Edition: An Intuitive Approach (Fokkink Wan)(Pevná vazba)

The new edition of a guide to distributed algorithms that emphasizes examples and exercises rather than the intricacies of mathematical models.This book offers students and researchers a guide to distributed algorithms that emphasizes examples and exercises rather than the intricacies of

Poems That Solve Puzzles: The History and Science of Algorithms (Bleakley Chris)(Pevná vazba)

Algorithms are the hidden methods that computers apply to process information and make decisions. Nowadays, our lives are run by algorithms. They determine what news we see. They influence which products we buy. They suggest our dating partners. They may even be determining the outcome of national