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

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

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

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

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,

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

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,

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

Machine Learning with Python for Everyone (Fenner Mark)(Paperback)

The Complete Beginner's Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you're an absolute beginner. If you can

Advances in Financial Machine Learning (Lopez de Prado Marcos)(Pevná vazba)

Machine Learning in Financial Markets begins by structuring financial data in a way that is amenable to machine learning (ML) algorithms. Then, the author discusses how to conduct research with ML algorithms on that data and how to backtest your discoveries. Most of the problems and solutions are

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

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

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

Neural Machine Translation (Koehn Philipp)(Pevná vazba)

Deep learning is revolutionizing how machine translation systems are built today. This book introduces the challenge of machine translation and evaluation - including historical, linguistic, and applied context -- then develops the core deep learning methods used for natural language applications

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

Understanding Normal and Clinical Nutrition (Rolfes Sharon Rady)(Pevná vazba)

Nourish your mind with UNDERSTANDING NORMAL AND CLINICAL NUTRITION, 12th Edition! Start by learning about normal nutrition, including the effects of food and nutrients on your mental and physical health, and then turn your focus to the clinical side of nutrition and the therapeutic care of people

Prediction, Learning, and Games (Cesa-Bianchi Nicolo)(Pevná vazba)

This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual

Curaille i gCeannas (Nic Sheain Caitriona)(Pevná vazba)

Gaussian Processes for Machine Learning (Rasmussen Carl Edward)(Pevná vazba)

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received

Wedges (Rustad Martha E. H.)(Pevná vazba)

Beginner readers and budding young scientists will love learning about using simple machines in everyday situations. Along the way, readers will be supported by expertly levelled text, a strong text-photo match and appropriate text load. Readers will be excited to dig in and learn all about simple

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