Unit information: Advanced Topics in AI in 2019/20

Unit name	Advanced Topics in AI
Unit code	COMSM0028
Credit points	10
Level of study	M/7
Teaching block(s)	Teaching Block 2 (weeks 13 - 24)
Unit director	Professor. Santos-Rodriguez
Open unit status	Not open
Pre-requisites	None
Co-requisites	None
School/department	School of Computer Science
Faculty	Faculty of Engineering

Description including Unit Aims

This seminar-style unit introduces advanced and state-of-the-art topics in AI. There will be a mix of presentations by academics and students. The goal of the unit is to both improve the breadth and depth of general AI knowledge and to learn how to process and present scientific material.

The selected topics are chosen to be practically applicable and make students reflect about future research directions. Some topics might not strictly AI but related; they are included to understand the wider context of AI. Examples of topics to be covered in the first year include: Explainable and Interpretable AI; Reinforcement learning; Experimental design; Evaluation and psychometrics.

Intended Learning Outcomes

Upon successful completion of the unit students will be able to:

1. identify and describe the wider context in which AI systems operate;
2. demonstrate an understanding of selected topics in advanced AI, such as reinforcement learning and explainable AI;
3. demonstrate an understanding of selected topics in relevant scientific methodology, such as experimental design and psychometrics;
4. process and present scientific material to a peer audience.

Teaching Information

This unit will be made up of a combination of taught seminars and problem classes.

Assessment Information

The unit will be assessed through a presentation (talk) on one or more of the topics covered (30%) and a written report (2,000 words, 70%). Both test all ILOs.

Reading and References

Selected literature, references and online material will be provided at the start of the unit