Part I: Introduction to Machine Learning1. Types of ML methods (supervised, unsupervised, semi-supervised; classification, regression)
2. Dealing with small labeled datasets (semi-supervised learning, active learning)
3. Selecting a methodology and evaluation metrics
4. Interpreting and explaining model behavior
5. Hyperparameter optimization and training neural networks Part II: Methods of machine learning6. The new and unique challenges of planetary missions
7. Data acquisition (PDS nodes, etc.) and Data types, projections, processing, units, etc. Part III: Useful tools for machine learning projects in planetary science8. The Python Spectral Analysis Tool (PySAT): A Powerful, Flexible, Preprocessing and Machine Learning Library and Interface
9. Getting data from the PDS, pre-processing, and labeling it Part IV: Case studies10. Enhancing Spatial Resolution of Remotely Sensed Imagery Using Deep Learning and/or Data Restoration
11. Surface mapping via unsupervised learning and clustering of Mercury's Visible-Near-Infrared reflectance spectra
12. Mapping Saturn using deep learning
13. Artificial Intelligence for Planetary Data Analytics - Computer Vision to Boost Detection and Analysis of Jupiter's White Ovals in Images Acquired by the Jiram Spectrometer