Unit information: Data-Driven Computer Science in 2020/21

Unit name	Data-Driven Computer Science
Unit code	COMS20011
Credit points	10
Level of study	I/5
Teaching block(s)	Teaching Block 2 (weeks 13 - 24)
Unit director	Dr. Aitchison
Open unit status	Not open
Pre-requisites	COMS10014 Mathematics for Computer Science A
Co-requisites	None
School/department	School of Computer Science
Faculty	Faculty of Engineering

Description including Unit Aims

This unit seeks to acquaint students with the fundamental aspects of processing digital data, presented in the context of concrete examples from applications in machine learning, data mining, and (1D/2D) signal processing.

Particular emphasis is placed on the importance of representation and modelling.

Intended Learning Outcomes

On successful completion of this unit, students will:

1. Demonstrate understanding of how audio, video, graphical objects, etc are represented digitally.
2. Appreciate the role of representation, feature extraction, modelling, estimation, clustering, and classification in digital data processing.
3. Appreciate the differences and commonalities between data processing tasks.
4. Demonstrate understanding of the role of training/learning in modelling, classifying and clustering.
5. Be confident working with high-dimensional spaces and associated transformations.
6. Be able to analyse data processing problems and decide what techniques to apply.

Teaching Information

Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities supported by drop-in sessions, problem sheets and self-directed exercises.

Assessment Information

60% summer timed assessment, 40% coursework.

Reading and References

Course notes will be provided at the start of the lectures. The following book contains material for advanced study:

• Duda, Richard, Hart, Peter and Stork, David, Pattern Classification, Second edition (John Wiley and Sons, 2000) ISBN: 978-0471056690