This is an advanced postgraduate course that offers students the opportunity to study a special thematic area within the discipline of Artificial Intelligence.
The topics will vary from year to year in response to emerging theoretical and practical issues in the discipline, as well as the research interests and expertise of academics and sessional staff. They will be drawn from the broad areas of Artificial Intelligence, and could include (but not limited to) the following topics: Planning, Scheduling, Games, Search, Reasoning (constraint-based, model-based, spatial, temporal), Knowledge representation, Decision-making under uncertainty, Integrated planning and learning, Reinforcement learning, and Robotics.
Please see the class website for the specific topics covered in a particular semester.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Gain both a wide and a deep knowledge of the topic(s) taught in the current instance of the course.
- Navigate through, and critically examine, the scientific literature on the taught topic(s).
- Plan and execute project work and/or a piece of research and scholarship in the advanced topic(s)
- Reflect on current approaches of the advanced topic(s) and evaluate their limitations
Indicative Assessment
- Assignments (55) [LO 1,2,3,4]
- Final Exam and/or Project (45) [LO 1,2,3]
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
A total of 130 hours including lectures, tutorials and self study.
Inherent Requirements
None
Requisite and Incompatibility
Prescribed Texts
None
Preliminary Reading
We will not follow one particular textbook, rather we will use appropriate chapters of books or research papers.
Assumed Knowledge
Students are assumed to have solid background knowledge in general computer science (e.g., programming experience; some basic theoretical CS), but no specialist knowledge.
Fees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2024 | $4980 |
- International fee paying students
| Year | Fee |
|---|---|
| 2024 | $6360 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 9222 | 22 Jul 2024 | 29 Jul 2024 | 31 Aug 2024 | 25 Oct 2024 | In Person | N/A |
