The 2016 bootcamp consisted of three days of lectures covering data processing, supervised learning and unsupervised learning as well as hands-on exercises using MATLAB covering a range of data analysis topics touching on each of the lecture . Example topics include:
- Identifying important features in complex/high dimensional data
- Visualizing high dimensional data to facilitate user analysis.
- Identifying the fabrication 'descriptors' that best predict variance in functional properties.
- Quantifying similarities between materials using complex/high dimensional data
The hands-on exercises focused on demonstrating practical use of machine learning tools on real materials data. Attendees will learn to analyze a range of data types from scalar properties such as material hardness to high dimensional spectra and micrographs.