2 x 50-minute lectures weekly
10 x 50-minute seminars per semester
10 x 2-hour computer labs per semester
1 Admission into MIT-Master of Information Technology
2 36 units of topics
3 Admission into MSCCS-Master of Science (Computer Science)
3a Admission into MCS-Master of Computer Science
3b Admission into MCSAI-Master of Computer Science (Artificial Intelligence)
3c Admission into MDSC-Master of Data Science
Must Satisfy: ((1 and 2) or ((3 or 3a or 3b or 3c)))
Enrolment not permitted
1 of COMP3742, COMP8742 has been successfully completed
Topic description
This topic will provide a basic understanding of Intelligent Systems and their applications, including a basic understanding and broad overview of Artificial Intelligence and Heuristic Search methodologies, and supervised and unsupervised Machine Learning and Data Mining technologies. The topic provides a balance of theory and practice, with two lectures on theory or philosophy being complemented by seminar-style case studies on specific applications, including guest speakers. Practical work is largely undertaken and assessed in weekly computing laboratory sessions, with a variety of assignments including specification, programming and written tasks. Specific areas to be covered include methodologies and philosophical issues, the relationship of intelligent systems to human intelligence and cognitive science, applications to knowledge engineering, knowledge discovery, language processing, text and data mining, as well as selected topics relating to cognition, perception, visualization and classification/clustering.
Educational aims
Educational aims of the topic:
  1. To be able to specify, consult, deploy and implement intelligent software and systems
  2. To understand and be able to employ Logic Programming, Knowledge Engineering and Knowledge Discovery paradigms and frameworks
  3. To be able assess requirements and select or adapt appropriate methodologies, techniques and tools for the achievement of a task, including through interviewing personnel and reviewing literature
  4. To be able to communicate with non-computing specialists and interdisciplinary specialists in the design of intelligent systems, learning paradigms or data mining applications
  5. To develop an ability to work alone both in a team, including to integrate work and build extensible systems, and to negotiate an ethical solution acceptable to a diverse range of stakeholders
  6. To understand and be able to work with the formalisms of the artificial intelligence, machine learning and formal grammars, and be able to approach research papers in this area or that assume a basic knowledge theoretical background
Expected learning outcomes
At the completion of this topic, students are expected to be able to:

  1. Understand concept of heuristics and be able to develop, design and prove desired properties of intelligent systems and developing learning and mining algorithms
  2. Understand the mathematical and theoretical foundations to deal with the broad range of intelligent systems, applications and literature, including producing and critiquing proposals and reports appropriate to the field
  3. Weigh up the complex interactions among human factors, personal and team relationships and ethical considerations and risks that affect the design and employment of intelligent systems
  4. Achieve substantial and surprisingly complex and intelligent behaviours in simple programs and systems
  5. Develop a deep insight into the power of heuristics and tricks and the human propensities that make for a good or a bad interface, and that either easily convince the user into accepting behaviour as human or conversely irk the user as being somehow inhuman
  6. Become adept at styles of programming and system development that are knowledge, rule and logic based rather than depending on low level step by step instructions