At the end of this module, you will be able to:
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Specify and interpret the central concepts underpinning supervised, unsupervised, and reinforcement learning.
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Describe approaches for materials representation, including chemical composition and crystal structure.
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Discover structure and property information from public databases using Python.
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Compare a range of classical machine learning and deep learning approaches.
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Train and evaluate machine learning models for materials problems.