Applications of Machine Learning in Plant Science

Overview

What you'll learn

• describe key concepts in machine learning,
• identify examples of how machine learning can be applied in the plant sciences,
• implement machine learning approaches in the plant sciences, and evaluate these implementations.

Syllabus 11

1. Introduction
2. Supervised and Unsupervised Learning
3. Training and Test Sets
4. Intro. to Supervised Learning: Linear Regression
5. Decision Trees and Random Forests
6. Cross-Validation, Grid Search and Model Follow-up
7. Intro. to Unsupervised Learning and Clustering
8. K-means Clustering
9. Dimension Reduction
10. Intro. to Neural Networks
11. Training and Backpropagation

Skills you'll practise

Artificial Neural NetworksMachine LearningAuditingPlant ScienceApplied Machine LearningPython (Programming Language)JupyterBiologySupervised Learning

Advantages and disadvantages

Some points apply to every course of this kind; see how we rank.

Before you start

Introductory-level knowledge of Python recommended. Also helpful, familiarity or interest in plant sciences; Introductory-level knowledge of statistics.

Taught by

• Adrian PowellDirector, BTI Computational Biology Center
• Gaurav MogheAssistant Professor, School of Integrative Plant Science, Plant Biology Section