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

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

Natural Language Processing: A Machine Learning Perspective (Zhang Yue)(Pevná vazba)

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature,

Machine Learning: An Algorithmic Perspective, Second Edition (Marsland Stephen)(Pevná vazba)

A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning

Mathematics for Machine Learning (Deisenroth Marc Peter)(Pevná vazba)

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or

Understanding Machine Learning (Shalev-Shwartz Shai)(Pevná vazba)

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental

Probabilistic Robotics (Thrun Sebastian)(Pevná vazba)

An introduction to the techniques and algorithms of the newest field in robotics.Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots

A First Course in Machine Learning (Rogers Simon)(Pevná vazba)

'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

Bayesian Reasoning and Machine Learning (Barber David)(Pevná vazba)

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who

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

The Hundred-Page Machine Learning Book (Burkov Andriy)(Pevná vazba)

As its title says, it's the hundred-page machine learning book. It was written by an expert in machine learning holding a Ph.D. in Artificial Intelligence with almost two decades of industry experience in computer science and hands-on machine learning.This is a unique book in many aspects. It is

Pediatric Rehabilitation: Principles and Practice (Murphy Kevin P.)(Pevná vazba)

Note to Readers: Publisher does not guarantee quality or access to any included digital components if book is purchased through a third-party seller.This revised and greatly expanded sixth edition of Pediatric Rehabilitation continues to set the standard of care for clinicians and remains the

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

Hands-On Machine Learning with R (Boehmke Brad)(Pevná vazba)

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today's most popular machine learning methods. This book serves as a practitioner's guide to the machine learning process and is meant to help the reader learn to apply the machine

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

Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning (Gilliland Michael)(Pevná vazba)

Discover the role of machine learning and artificial intelligence in business forecasting from some of the brightest minds in the field In Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning accomplished authors Michael Gilliland, Len Tashman, and Udo Sglavo

Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent (Zhou Jianlong)(Pevná vazba)

Part I Transparency in Machine Learning.- Part II Visual Explanation of Machine Learning Process.- Part III Algorithmic Explanation of Machine Learning Models.- Part IV User Cognitive Responses in ML-Based Decision Making.- Part V Human and Evaluation of Machine Learning.- Part VI Domain Knowledge

Foundations of Probabilistic Programming (Barthe Gilles)(Pevná vazba)

This book provides an overview of the theoretical underpinnings of modern probabilistic programming and presents applications in e.g., machine learning, security, and approximate computing. Comprehensive survey chapters make the material accessible to graduate students and non-experts. This title

Machine Learning in Asset Pricing (Nagel Stefan)(Pevná vazba)

A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments,

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

International Perspectives on the Teaching and Learning of Geometry in Secondary Schools (Herbst Patricio)(Pevná vazba)

This book presents current perspectives on theoretical and empirical issues related to the teaching and learning of geometry at secondary schools. It contains chapters contributing to three main areas. A first set of chapters examines mathematical, epistemological, and curricular perspectives. A

Corporations and Sustainability: The South Asian Perspective (P. D. Jose)(Pevná vazba)

Corporations and Sustainability: The South Asian Perspective is a collection of contributions from leading academics and practitioners which provides an overview of the key challenges and opportunities related to sustainability in South Asia. The last two decades have seen rapid and often dramatic

Introduction to Machine Learning with Applications in Information Security (Stamp Mark)(Pevná vazba)

Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn't prove theorems, or otherwise dwell on mathematical theory

Teacher Learning with Classroom Assessment: Perspectives from Asia Pacific (Jiang Heng)(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