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.
Faculty
Chief examiner(s)
Unit guides
Synopsis
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.
Outcomes
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.
Assessment
Examination (3 hours): 60%; In-semester assessment: 40%
Workload requirements
Minimum total expected workload equals 12 hours per week comprising:
- Contact hours for on-campus students:
- Two hours of lectures
- One 1-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