A practical introduction to the foundations of machine learning.

3 hours per week.

Machine Learning (H)

None

Examination 50%, coursework 50%.

To present students with an introduction to the general theory of learning from data and to a number of popular Machine Learning methods.  To present students with the practical application of Machine Learning techniques in a variety of domains, including Human Computer Interaction, Information Retrieval, Bioinformatics and Computer Vision and Graphics.

By the end of the course students will be able to:

1. Demonstrate knowledge of the major machine learning application areas in, for example Information Retrieval, Human Computer Interaction, Bioinformatics and Computer Vision & Graphics;

2. Explain the principle of learning from data;

3. Implement and use machine learning algorithms in Python;

4. Apply the main machine learning methods: regression, classification, clustering, probability density estimation and dimensionality reduction;

5. Contrast the strengths and weaknesses of different algorithms for different tasks and datasets;

6. Analyse the benefits and drawbacks of some advanced machine learning approaches, e.g. non-parametric methods, sampling techniques and neural networks.