Model-Based Machine Learning

Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains.A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem.This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system. The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications.Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions.Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach.The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem. Features