Information-Driven Machine Learning: Data Science as an Engineering Discipline

This groundbreaking book transcends traditional machine learning approaches by introducing information measurement methodologies that revolutionize the field.

Stemming from a UC Berkeley seminar on experimental design for machine learning tasks, these techniques aim to overcome the 'black box' approach of machine learning by reducing conjectures such as magic numbers (hyper-parameters) or model-type bias. Information-based machine learning enables data quality measurements, a priori task co...