Full course description
Bootcamp (Days 1-4)
Four days of lectures and hands-on exercises covering a range of data analysis topics from introduction to python and data pre-processing to advanced machine learning analysis techniques. Example topics include:
- Identifying important features in complex/high dimensional data
- Visualizing high dimensional data to facilitate user analysis.
- Identifying the 'descriptors' that best predict variance in functional properties.
- Quantifying similarities between materials using complex/high dimensional data
- Identifying the most informative experiment to perform next.
Hands-on exercises will include practical use of machine learning tools on real materials experimental data (scalar values, spectra, micrographs, etc.)
Scientists will also demonstrate how they performed recently published research, from loading and preprocessing data to analyzing and visualizing results, all in Jupyter notebooks. Day 4 will include hand-on exercises on how to use the AFLOW database online.
If you are a student (graduate, undergraduate, or high school), write to us first at MLMR@umd.edu, so we can send you a student discount code BEFORE you register.