| 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 |
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.
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.
2 hours per week (lectures) + 1 hour drop-in class
Coursework – Implement AI algorithms in an appropriate language and written report on the results (50%) (ILO 2-5)
2 Hour Exam (50%) (All ILOs)
Stuart J. Russell and Peter Norvig, Artificial Intelligence: Modern Approach, (2nd Edition)
