PLSCI 7202 - Applications of Machine Learning to Plant Science

Fall. 2 credits. Letter grades only.

Enrollment limited to: graduate students. Undergraduates must obtain permission of instructor. This module can be taken independently of PLSCI 7201 and PLSCI 7203.

C. De Sa, G. Moghe, S. Strickler.

This course will start with a brief refresher on the command line and programming basics as well as data and code management best practices. Students will be given an introduction to machine learning including supervised learning, test validation, learning via gradient methods, neural networks, logistic regression, deep learning, and parameter optimization. Applications of these methods to problems in the plant sciences will be reviewed. In-class problems, hack-a-thons, and a final team presentation will enable students to apply the methods learned to questions in plant science.

Outcome 1: Implement data and code management best practices.

Outcome 2: Apply proper programming techniques and ML principles to real data, avoiding common pitfalls.

Outcome 3: Conduct integrative research with scientists across disciplinary boundaries.