Machine Learning for Image Data

Become an expert in machine learning for bioscience

Machine learning has made it possible to process vast quantities of image data. That means it can enhance and facilitate the work of bioscience researchers, particularly the field of plant phenotyping.

On this five-week course from the University of Nottingham, you’ll gain an overview of the applications of machine learning for image data, focusing specifically on its use in plant phenotyping.

Gain an overview of machine learning as it applies to biological image data

You’ll start the course with an overview of machine learning, and an introduction to image data and features.

You’ll gain the background you need to understand and apply machine learning in your own bioscience research.

Master common techniques and softwares for image analysis

Once you’ve mastered the principles of machine learning for image data, you’ll start building the practical skills you need to navigate machine learning software.

Weeks 3 and 4 of the course will cover the main techniques for processing image data, some common challenges surrounding these, and useful tips and tricks to help you overcome them.

Whether you want to model data through a decision tree or create visualisations using Python, you’ll gain the hands-on experience you need for your research.

Understand neural networks and deep learning

In your last week of the course, you’ll look more closely at a specific subfield of machine learning: deep learning. You’ll learn how neural networks can be used to process biological images in the same way the human brain would.

By the end of the course, you’ll have an understanding of how machine learning can be used with biological image data, and the skills you need to harness it in your own bioscience research.

This course is designed for researchers and other professionals working in plant phenotyping or related bioscience disciplines, who want to know more about how machine learning can be used with image data.

Any software needed for the course is available to download for free and introduced as part of the course content.

• What is machine learning?
• Introduction to machine learning
• Example machine learning problems
• Supervised versus unsupervised learning
• Common tasks - classification and regression
• Software tools
• Summary and review
• Data and features
• Types of data and features
• Feature extraction
• Labelling image data
• Pre-processing data
• Summary and review
• Common techniques
• Introduction
• Clustering
• Classification
• Regression
• Evaluation, visualisation, and selection
• Summary and review
• Tips and tricks
• Introduction
• Good Training Practice
• Data augmentation
• Common challenges
• Summary and review
• Deep learning
• What is deep learning?
• Neural networks and deep learning
• Some simple tools
• Summary and review