Introduction to Python and Machine Learning
The event will introduce materials researchers from industry, national laboratories, and academia to machine learning theory and tools for rapid materials data analysis.
Three days of lectures and hands-on exercises covering a range of data analysis topics from data pre-processing through 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 fabrication ‘descriptors’ that best predict variance in functional properties.
• Quantifying similarities between materials using complex/high dimensional data
The hands-on exercises will demonstrate practical use of machine learning tools on real materials data (scalar values, spectra, micrographs, etc.).
Demonstrations and Talks by Top Researchers
Antoine Georges (CCQ)
Mike Norman (ANL)
Stefano Curtarolo (Duke)
Sergei Kalinin (ORNL)
Ichiro Takeuchi (UMD)