Learning Outcomes

At the end of this course, you will be able to:

• Specify and interpret the central concepts underpinning supervised, unsupervised, and reinforcement learning.

• Describe approaches for materials representation including chemical composition and crystal structure.

• Discover structure and property information from public databases using Python.

• Compare a range of classical machine learning and deep learning approaches.

• Train and evaluate machine learning models for chemical problems.