Machine Learning: Theory and Practice

Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning, including regression, tree-based methods, artificial neural networks, reinforcement learning, and unsupervised learning. It is designed for advanced undergraduate or beginning graduate students and mathematically and/or programming-oriented individuals who want to learn machine learning on their own.

Format: Hardback
Length: 282 pages
Publication date: 21 December 2022
Publisher: Taylor & Francis Ltd

Machine Learning: Theory and Practice is a comprehensive guide that provides an introduction to the most popular methods in machine learning. This book covers a wide range of topics, including regression, tree-based methods such as Random Forests and Boosted Trees, artificial neural networks such as Convolutional Neural Networks (CNNs), reinforcement learning, and unsupervised learning focused on clustering.

The explanations are presented in a clear and concise manner, accompanied by illustrative figures and examples to enhance understanding. Each machine learning method discussed is accompanied by appropriate libraries in the R programming language, along with programming examples to facilitate practical application.

Designed with the needs of advanced undergraduate and beginning graduate students in mind, this book offers a straightforward and accessible presentation of commonly used machine learning algorithms. It caters to individuals with a mathematical and/or programming background who are interested in learning machine learning on their own.

The book is structured in a way that allows for a thorough understanding of the topics covered. It begins with an overview of machine learning concepts and theories, followed by detailed explanations of the algorithms. Each chapter includes mathematical details to ensure a firm grasp of the underlying principles, enabling readers to explore further.

Furthermore, the book includes worked-out programming examples that reinforce the theoretical concepts and help readers apply the machine learning methods in real-world scenarios. These examples provide a comprehensive understanding of the methods, enabling students to develop practical skills and apply them to their own research or projects.

Overall, Machine Learning: Theory and Practice is an invaluable resource for anyone seeking to gain a comprehensive understanding of machine learning. Whether you are a student, researcher, or professional looking to advance your knowledge in this field, this book will provide you with the foundations and practical skills necessary to succeed.

Weight: 558g
Dimension: 163 x 240 x 24 (mm)
ISBN-13: 9780367433543