Machine Learning Tools for Modeling Energy Transport and Conversion Processes

3 credits · Graduate

This course teaches students how machine learning tools work and their effective use in energy related research and technology development. This course first covers basic probability, linear algebra concepts, and foundation mathematics principles used in machine learning tools. Python programming will be used in class projects. Students will construct a genetic algorithm and a neural network model from scratch to explore basic features of these tools, and will then use Python neural network programming tools to develop models for energy conversion and energy transport process applications. Students will explore different machine learning methods in 3 assigned projects and can construct a final project in an application of interest to them.