Free Download Information-Driven Machine Learning: Data Science as an Engineering Discipline
English | 2024 | ISBN: 3031394763 | 487 Pages | PDF EPUB (True) | 17 MB
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 complexity estimations, and reproducible design of data science experiments. The benefits include significant size reduction, increased explainability, and enhanced resilience of models, all contributing to advancing the discipline's robustness and credibility.