Model-Based Machine Learning

This book provides a comprehensive guide to machine learning, covering topics such as assumptions, concepts, and problem diagnosis. It offers practical insights through real-world case studies and includes full source code for reproducibility.

Format: Hardback
Length: 455 pages
Publication date: 28 September 2023
Publisher: Taylor & Francis Inc

Machine learning systems are intricate frameworks that rely on a multitude of assumptions to function effectively. These assumptions can significantly impact the accuracy and reliability of the systems when applied to real-world problems. In this comprehensive guide, we delve into the underlying assumptions made by machine learning systems and explore their implications. We begin by examining the fundamental concepts of machine learning, such as supervised and unsupervised learning, and their applications in various domains. We then present real-world case studies that illustrate the practical challenges and solutions associated with machine learning.

Throughout the guide, we emphasize the importance of diagnosing, understanding, and addressing problems that may arise in machine learning systems. We provide practical techniques and methodologies for troubleshooting and improving the performance of these systems. Additionally, we offer full source code availability, allowing readers to reproduce and explore our models and results. For those interested in delving deeper into the mathematical intricacies of inference algorithms, we include optional deep-dive sections that provide detailed explanations and calculations.

By exploring the assumptions behind machine learning systems and applying them in real-world scenarios, this guide aims to empower readers with the knowledge and skills necessary to build robust and reliable machine learning applications. Whether you are a data scientist, engineer, or business professional, this guide will provide you with the tools you need to succeed in the rapidly evolving field of machine learning.

Weight: 1030g
Dimension: 234 x 156 (mm)
ISBN-13: 9781498756815