Unit information: Introduction to Artificial Intelligence in 2020/21

Unit name	Introduction to Artificial Intelligence
Unit code	EMATM0044
Credit points	10
Level of study	M/7
Teaching block(s)	Teaching Block 2 (weeks 13 - 24)
Unit director	Professor. Lawry
Open unit status	Not open
Pre-requisites	Basic competency in Python or Matlab at the level of EMAT10007 or EMAT20920
Co-requisites	None
School/department	School of Engineering Mathematics and Technology
Faculty	Faculty of Engineering

Description including Unit Aims

This unit will provide a broad introduction to AI for MSc students in SCEEM. It will provide an overview of the most established AI and Machine Learning approaches and paradigms and give students the opportunity to implement AI algorithms and use relevant software tools. Areas covered will included supervised learning (classification and regression, e.g. neural networks), unsupervised learning (clustering), probabilistic methods (e.g. Bayesian networks and Markov decision processes), genetic algorithms, and multi-agent systems.

Intended Learning Outcomes

Upon successful completion of the course, students will be able to:

1. Be able to explain basic concepts and assumptions underpinning key AI algorithms
2. Rigorously compare the performance of competing methods.
3. Implement AI algorithms in a suitable programming language and toolboxes.
4. Apply machine learning to analyse data.
5. Modelling the behaviour of autonomous systems.

Teaching Information

Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities supported by drop-in sessions or online computer laboratories and problem sheets.

Assessment Information

1 Summative Assessment, 100% - Coursework. This will assess all ILOs.

Reading and References

Stuart J. Russell and Peter Norvig, Artificial Intelligence: Modern Approach, (2nd Edition)