Experimental Design for Machine Learning

Develop innovative strategies for machine learning with experimental design

Embark on a focused exploration into the core methodologies of experimental design tailored for machine learning, particularly within the context of plant phenotyping.

This course is designed to bridge the gap between theoretical knowledge and practical application, providing a comprehensive understanding of how experimental design principles can optimise machine learning outcomes.

Apply experimental design techniques in machine learning

Explore the foundational aspects of experimental design as it applies to machine learning. Understand the critical components of setting up experiments, from hypothesis formation to variable control and data analysis, which are crucial for achieving reliable results.

Investigate various experimental designs used in real-world machine learning scenarios, focusing on their applications in improving model reliability and performance.

Develop strategies for data collection and analysis for plant phenotyping

Delve into the strategies for effective data collection and annotation essential for training robust machine learning models.

Learn how to expand and refine datasets to cover a broad range of variables and conditions that will enhance the predictive power of your models.

Utilise techniques for model selection and performance

Sift through and select appropriate machine learning models and adjust parameters to maximise performance.

Discuss case studies demonstrating the successful application of these techniques in plant phenotyping.

By the end of this course, you’ll have a deep understanding of how experimental design supports machine learning, driving innovation in biosciences.

This course is designed for bioscience professionals, particularly those in plant phenotyping, looking to enhance their skills in experimental design and machine learning to improve data collection, analysis, and model implementation.

No specific software is required. One demonstration will use Python.

• Experimental design
• Welcome to the course
• Data collection and annotation
• Summary and review
• Understanding and working with data
• Organising your datasets
• Expanding your dataset
• Releasing data
• Summary and review
• Choosing and using models
• Software, model selection, and training
• Improving performance
• Summary and Review
• Trusting results
• Trusting results
• Interpreting output
• Improving results
• Summary and review
• Practical tips and tricks
• Tips and tricks
• Course summary