E-Books →Machine Learning for Planetary Science

The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation.
Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials
Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets
Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems
Utilizes case studies to illustrate how machine learning methods can be employed in practice