6 points, SCA Band 2, 0.125 EFTSL
Undergraduate - Unit
Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.
CSE2309, CSE3309, DGS3691
This unit includes history of artificial intelligence; intelligent agents; problem solving and search (problem representation, heuristic search, iterative improvement, game playing); knowledge representation and reasoning (extension of material on propositional and first-order logic for artificial intelligence applications, planning, frames and semantic networks); reasoning under uncertainty (belief networks); machine learning (decision trees, Naive Bayes, neural nets and genetic algorithms); language technology.
At the completion of this unit, students should be able to:
- describe the historical and conceptual development of AI; foundational issues for AI, including the frame problem and the Turing test;
- explain, apply and evaluate the goals of AI and the main paradigms for achieving them including logical inference, search, machine learning and Bayesian inference;
- explain the social and economic roles of AI;
- describe, analyse, apply and evaluate heuristic AI for problem solving;
- describe, analyse and apply basic knowledge representation and reasoning mechanisms;
- describe, analyse and apply probabilistic inference mechanisms for reasoning under uncertainty;
- describe, analyse, apply and evaluate machine learning techniques;
- describe, analyse, apply and evaluate the use of the above techniques in different domain, specifically language technology.
Examination (3 hours): 60%; In-semester assessment: 40%
Minimum total expected workload equals 12 hours per week comprising:
- Contact hours for on-campus students:
- Two hours of lectures
- One 2-hour laboratory
- Additional requirements (all students):
- A minimum of 9 hours independent study per week for completing lab and project work, private study and revision.
See also Unit timetable information