Model-Based Machine Learning

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 real-world 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 deep-dive sections with more mathematical details on inference algorithms for the interested reader.