FIT3080 - Intelligent systems - 2019

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:

1. describe the historical and conceptual development of AI; foundational issues for AI, including the frame problem and the Turing test;
2. explain, apply and evaluate the goals of AI and the main paradigms for achieving them including logical inference, search, machine learning and Bayesian inference;
3. explain the social and economic roles of AI;
4. describe, analyse, apply and evaluate heuristic AI for problem solving;
5. describe, analyse and apply basic knowledge representation and reasoning mechanisms;
6. describe, analyse and apply probabilistic inference mechanisms for reasoning under uncertainty;
7. describe, analyse, apply and evaluate machine learning techniques;
8. describe, analyse, apply and evaluate the use of the above techniques in different domain, specifically language technology.

NOTE: From 1 July 2019, the duration of all exams is changing to combine reading and writing time. The new exam duration for this unit is 3 hours and 10 minutes.

Examination (3 hours): 60%; In-semester assessment: 40%

Minimum total expected workload equals 12 hours per week comprising:

1. Contact hours for on-campus students:
   • Two hours of lectures
   • One 2-hour laboratory
2. 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

Advanced computer science