CornellX: Applications of Machine Learning in Plant Science

This course provides learners with an introduction to applications of machine learning in the plant sciences. Learners will be given an introduction to machine learning including supervised learning, test validation, learning via gradient methods, neural networks, regression, and parameter optimization, with examples of how these techniques can be used in the context of plant biology. We will learn about examples from scientists currently applying machine learning in the plant sciences. A series of Python exercises in Jupyter will enable learners to apply their learning to questions in plant science. By the end of the course, learners will be able to describe key concepts in machine learning, implement machine learning approaches in the plant sciences, and evaluate these implementations. The course is asynchronous and student-paced, and it is offered as audit-only. Assessments will primarily consist of self-assessments, such as short check-your-understanding quizzes.