ModelBased 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 modelbased 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 modelbased machine learning are introduced through a series of case studies involving realworld 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 stepbystep to solve it using a modelbased 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: Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems. Explains machine learning concepts as they arise in realworld case studies. Shows how to diagnose, understand and address problems with machine learning systems. Full source code available, allowing models and results to be reproduced and explored. Includes optional deepdive sections with more mathematical details on inference algorithms for the interested reader.