PLSCI 7202

Applications of Machine Learning to Plant Science

Course Description

Course information provided by the 2022-2023 Catalog. Courses of Study 2022-2023 is scheduled to publish mid-June.

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.

Permission Note Enrollment limited to: graduate students. Undergraduates must obtain permission of instructor.

Last 4 Terms Offered (None)

Outcomes

• Implement data and code management best practices.
• Apply proper programming techniques and ML principles to real data, avoiding common pitfalls.
• Conduct integrative research with scientists across disciplinary boundaries.

When Offered Fall.

Comments This module can be taken independently of PLSCI 7201 and PLSCI 7203.

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