Unit name | Introduction to Artificial Intelligence |
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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:
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.
1 Summative Assessment, 100% - Coursework. This will assess all ILOs.
Stuart J. Russell and Peter Norvig, Artificial Intelligence: Modern Approach, (2nd Edition)