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Gesamtkatalog

Catalogue complet

Advances in Analysis and Geometry

Tanaka H: Introduction to Potential
Theory

Introduction to Potential Theory

Maximal Operators and Weights

Stochastic Processes and Pseudo-Differential Equations

p-Adic Analysis

Differential and Difference Equations with Applications in Queueing Theory

Advances in Computers

The Convergence of Artificial Intelligence (AI) and 6G Communication Networks: The Needs and Implications: Volume 139

An accessible resource to help you puzzle out logic concepts Logic For Dummies, 2nd Edition explains all the varied ways we use logic in philosophy, science, and everyday life. College students taking a logic course and lifelong learners alike can benefit from this accessible guide to logic concepts--such as syllogisms, constructing proofs and refutations, propositional and predicate logic, symbolic logic, modal and fuzzy logic, deductive and inductive reasoning, and beyond. With real-world examples, fun logic problems, and fully worked out proofs, you have plenty of opportunities to follow along and apply what you've learned. Logic For Dummies, 2nd Edition helps you connect the logical dots! Inside: <ul> <li>Grasp formal and informal logic with clear explanations and practice problems</li> <li>See how logic shows up in everyday life and discover how to spot logical fallacies</li> <li>Work through logical proofs and refutations, with answer explanations to strengthen your understanding</li> <li>Sharpen your ability to reason through complex issues with truth tables</li> </ul> Logic For Dummies, 2nd Edition is the logical choice for anyone who wants or needs to learn the concepts, theories, and formulas of logical reasoning.

Logic For Dummies

Fundamentals and Applications

Graph Theory

Quantum Machine Learning (QML): Platform, Tools and Applications: Volume 140

Data-Directed Mathematical Modeling

Storytelling with Data Box Set

Graph Mining

Wiley Series in Probability and Statistics

New edition of a PROSE award finalist title on core concepts for machine learning, updated with the latest developments in the field, now with Python and R source code side-by-side Machine Learning is a comprehensive text on the core concepts, approaches, and applications of machine learning. It presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, density estimation, and dimension reduction. New content for this edition includes chapter expansions which provide further computational and algorithmic insights to improve reader understanding. This edition also revises several chapters to account for developments since the prior edition. In this book, the design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods, enabling readers to solve applied problems more efficiently and effectively. This book also includes methods for optimization, risk estimation, model selection, and dealing with biased data samples and software limitations - essential elements of most applied projects. Written by an expert in the field, this important resource: <ul> <li>Illustrates many classification methods with a single, running example, highlighting similarities and differences between methods</li> <li>Presents side-by-side Python and R source code which shows how to apply and interpret many of the techniques covered</li> <li>Includes many thoughtful exercises as an integral part of the text, with an appendix of selected solutions</li> <li>Contains useful information for effectively communicating with clients on both technical and ethical topics</li> <li>Details classification techniques including likelihood methods, prototype methods, neural networks, classification trees, and support vector machines</li> </ul> A volume in the popular Wiley Series in Probability and Statistics, Machine Learning offers the practical information needed for an understanding of the methods and application of machine learning for advanced undergraduate and beginner graduate students, data science and machine learning practitioners, and other technical professionals in adjacent fields.

A Concise Introduction

Machine Learning

Ziel dieses Lehrbuchs ist es, das Material des zweiten Semesters eines Vorlesungszyklus zur Analysis prägnant und verständlich darzustellen und darüber hinaus Ausblicke und Ergänzungen zu geben, die den Stoff lebendig werden lassen.&nbsp;Besonderer Wert wird auf die Motivation der zu behandelnden Themen gelegt. Ein Ziel des Buches ist es auch, die Studierenden gründlich - etwa durch Ausblicke - auf weiterführende Vorlesungen vorzubereiten. Es basiert auf Vorlesungen, die regelmäßig und seit vielen Jahren an der TU München abgehalten werden.Zahlreiche Abbildungen veranschaulichen die behandelten Konzepte und Ideen. Zudem ermöglicht dieses Lehrbuch den Zugriff auf ca. 200 interaktive Aufgaben in der Springer Nature Flashcards-App, mit denen Wissen und Verständnis überprüft werden kann – hervorragend geeignet auch zur Prüfungsvorbereitung.Die AutorenProf. Dr. Martin Brokate ist Professor i.R. für Mathematik an der Technischen Universität München.Prof. Dr. Johannes Zimmer ist Professor für Analysis und Modellbildung an der Technischen Universität München.Dr. Florian Lindemann ist Akademischer Rat an der Technischen Universität München.&nbsp;

Differentialrechnung im Mehrdimensionalen

Analysis 2

This comprehensive compendium addresses a critical need in the AI and machine learning era by bridging foundational information theory (IT) concepts with practical applications in statistical learning. Unlike traditional IT textbooks, this volume emphasizes how IT principles, such as entropy and source coding, underpin modern machine learning techniques like cross-entropy, decision trees, and evidence-lower bounds.This unique book connects IT with probabilistic inference, illustrated through real-world applications, such as decoding LDPC codes for error correction. The inclusion of the Lea probabilistic programming package is particularly valuable for pedagogy, offering students a hands-on tool to solve numerical problems and reinforce theoretical concepts.The useful reference text benefits professionals, researchers, academics and students in the fields of calculus and probability, communications and information sciences.

Introduction To Information Theory And Probabilistic Inference, An

An accessible introduction to mathematical infinity for the endlessly curious &nbsp; Infinity has traditionally been a topic for philosophers, physicists, and artists, and it can be large, scary, or highly metaphorical. For mathematicians, however, infinity is not frightening or hopelessly abstract (though it is certainly big) but rather is a normal part of their work. In this guide, award-winning professor and author Edward R. Scheinerman offers an exploration of infinity for the curious. This is real math for real people, and you do not need a mastery of calculus to understand and enjoy it. &nbsp; Through witty and accessible prose, along with many illustrations, Scheinerman demonstrates how infinity is used in algebra, geometry, and logic, touching on hyperreal numbers, the hyperbolic plane, transfinite ordinals, and more. He ends each chapter with tantalizing questions to inspire readers toward further—infinite?—investigation. This book, by the author of The Mathematics Lover’s Companion, is for students, mathophiles, engineers, scientists, accountants—even people who fear mathematics!

Ten Mathematical Journeys

A Guide to Infinity

Introduction to Potential Theory

Autres titres de la collection: Advances in Analysis and Geometry

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