Machine learning is an exciting set of tools and techniques for classifying and analysing data. This course will introduce the state of the art in machine learning and provide some hand-on experience with data handling and analysis.
Next session: Spring 2019
Those interested in applying machine learning approaches to their work.
Artificial intelligence is an area rich with possibilities. Machine learning (ML) is a major subfield of AI, that provides a powerful set of tools and approaches to solving problems in data analysis and classification. These approaches have proved successful across a range of biological domains from protein homology prediction, through sequence analysis to image based phenotying.
In this short course, we'll provide a beginner's guide to Machine Learning. This will involve understanding what state of the art is in the field, how to recognise when ML approaches are likely to be useful, how to prepare data for analysis and how to apply various techniques. The course will involve a mixture of talks and hands-on material.
By the end of the course you should be able to:
Understand machine learning terminology
Identify problem domains where machine learning will be useful
Carry out some basic techniques for data cleaning and visualisation