Overview
This unit introduces the main problems and approaches to designing intelligent software systems including automated search methods, knowledge representation and reasoning, planning, reasoning under uncertainty, machine learning paradigms, and evolutionary algorithms.
Offerings
S1-01-CLAYTON-FLEXIBLE
S1-01-MALAYSIA-ON-CAMPUS
S1-01-OS-CHI-SEU-ON-CAMPUS
S2-01-CLAYTON-FLEXIBLE
S2-01-MALAYSIA-ON-CAMPUS
Requisites
Prerequisite
Prohibition
Contacts
Chief Examiner(s)
Dr Mor Vered
Professor Jianfei Cai
Unit Coordinator(s)
Dr Asad Malik
Learning outcomes
On successful completion of this unit, you should be able to:
1.
Explain the theoretical foundations of Artificial Intelligence (AI) - such as rational agency and symbolic and data-driven reasoning - that underpin the application to information technology and society;
2.
Critically explain, evaluate and apply appropriate AI theories, models and/or techniques in practice - including logical inference, heuristic search, genetic algorithms, machine learning and Bayesian inference;
3.
Utilise appropriate software tools to develop AI models or software;
4.
Utilise and explain evaluation criteria to measure the correctness and/or suitability of models.
Teaching approach
Peer assisted learning
Assessment
1 - Weekly quizzes
2 - Assignment
3 - Knowledge Representation
4 - Lab: Bayesian networks
5 - Lab: Machine learning
Scheduled and non-scheduled teaching activities
Laboratories
Seminars
Workload requirements
Workload
Learning resources
Required resources
Technology resources
Availability in areas of study
Computer science
Data science
Computer networks and security
Data science
Computer networks and security