Artificial Intelligence Techniques PG (6685.5)

Please note these are the 2026 details for this unit
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Artificial Intelligence Techniques PG
Available teaching periods Delivery mode Location
View teaching periods On-campus
 Bruce, Canberra
EFTSL Credit points Faculty
0.125 3 Faculty Of Science And Technology
Discipline Study level HECS Bands
Academic Program Area - Technology Post Graduate Level Band 2 2021 (Commenced After 1 Jan 2021)
Band 3 2021 (Commenced Before 1 Jan 2021)
This unit looks at those techniques and concepts implemented as software that carries out actions which are normally thought of as requiring intelligence when carried out by humans. Topics are selected from knowledge representation, heuristic search and problem reduction, AI algorithms, natural language processing, decision support systems, expert systems, computer assisted instruction, speech understanding, vision, theorem proving, pattern recognition, robotics, games, data mining, scheduling, constraint processing and optimisation.

Learning outcomes

From this unit, the students will be able to define and appraise the major areas of AI research and use. They will be able to write simple programs to demonstrate aspects of AI using a suitable programming language.


Graduate attributes

1. UC graduates are professional - communicate effectively
1. UC graduates are professional - employ up-to-date and relevant knowledge and skills
1. UC graduates are professional - take pride in their professional and personal integrity
1. UC graduates are professional - use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems
2. UC graduates are global citizens - behave ethically and sustainably in their professional and personal lives
2. UC graduates are global citizens - make creative use of technology in their learning and professional lives
3. UC graduates are lifelong learners - evaluate and adopt new technology
1. UC graduates are professional - display initiative and drive, and use their organisation skills to plan and manage their workload
1. UC graduates are professional - work collaboratively as part of a team, negotiate, and resolve conflict


Prerequisites

11512 Pattern Recognition and Machine Learning PG

Corequisites

11521 Programming for Data Science G OR 8936 Introduction to Information Technology G

Incompatible units

None.

Equivalent units

None.

Assumed knowledge

None.
Availability for enrolment in 2026 is subject to change and may not be confirmed until closer to the teaching start date.
Year Location Teaching period Teaching start date Delivery mode Unit convener
2025 Bruce, Canberra Semester 1 03 February 2025 On-campus Dr Ram Subramanian
2026 Bruce, Canberra Semester 1 16 February 2026 On-campus Dr Ram Subramanian
The information provided should be used as a guide only. Timetables may not be finalised until week 2 of the teaching period and are subject to change. Search for the unit timetable.

Required texts

All requisite material will be provided as part of the Lecture Slides. However, students may refer to the following textbooks for reference. E-versions of these books are available on the Internet, or can be referred to from the UC Library.

1. Pattern Recognition and Machine Learning by Christopher M. Bishop, Springer 2006.

2. Foundations of Data Science by Avrim Blum, John Hopcroft and Ravindran Kannan, Cambridge University Press, 2020. 

3. Statistical Methods for Psychology by David C. Howell (8th Edition), Cengage Learning, 2012.

4. Python Data Science Handbook, Essential Tools for Working with Data, Jake VanderPlas, Publisher: O'Reilly Media, November 2016

Submission of assessment items

Extensions & Late submissions

Assessment item submission format:

The first page of each assessment item should include the following information:

  • Student ID number and Name
  • A signed declaration by the student that the solution submitted represents original work.

Special assessment requirements

(1) In order to pass the unit, a student needs to score a minimum aggregate of 50% over all assessment items.

(2) The unit convenor reserves the right to question students on any of their submitted work for moderation and academic integrity purposes.

Students must apply academic integrity in their learning and research activities at UC. This includes submitting authentic and original work for assessments and properly acknowledging any sources used.

Academic integrity involves the ethical, honest and responsible use, creation and sharing of information. It is critical to the quality of higher education. Our academic integrity values are honesty, trust, fairness, respect, responsibility and courage.

UC students have to complete the Academic Integrity Module annually to learn about academic integrity and to understand the consequences of academic integrity breaches (or academic misconduct).

UC uses various strategies and systems, including detection software, to identify potential breaches of academic integrity. Suspected breaches may be investigated, and action can be taken when misconduct is found to have occurred.

Information is provided in the Academic Integrity Policy, Academic Integrity Procedure, and University of Canberra (Student Conduct) Rules 2023. For further advice, visit Study Skills.

Learner engagement

Activity

Unit Workload (hrs) - as a guide

Lectures Attendance (1.5 hrs x 12 weeks)

18

Lectures Preparation (1.5 hrs x 12 weeks) 

18

Tutorials Attendance (1 hr x 11 weeks)

11

Tutorials Preparation (1 hr x 11 weeks)

11

Ass 1 – EM for colour image segmentation

5

Ass 2 – Topic Modelling

5

Mid-semester exam

10

Project End-sem (Code, Report & Presentation) 

72

Total 

150

Participation requirements

Students are strongly encouraged to attend all lectures and labs for successful completion of the unit.Overall a minimum score of 50% over all weighted assessments is required to pass the unit. Participation in tutorial/programming sessions is strongly encouraged of this unit, and attendance will be recorded. In addition to a 50% aggregate, you must complete and submit at least 4 (four) lab exercises out of 8 on Canvas over the semester to pass the unit. Experience has shown that students who do not attend the classes and/or tutorials will have difficulty in passing the subject.

Required IT skills

Students are expected to know Python to successfully complete the unit. Introductory Python programming will be covered in Lab Session 1, and some additional resources will be provided. Matlab programming resources can be retreieved from the Internet. For the assignment and project reports, knowledge of Latex (https://www.overleaf.com/learn/latex/Learn_LaTeX_in_30_minutes) or MS Word would be required.

Occasionally, the unit may involve online meetings in real time using the Virtual Room in your UCLearn teaching site. The Virtual Room allows you to communicate in real time with your lecturer and other students. To participate verbally, rather than just typing, you will need a microphone. For best audio quality we recommend a microphone and speaker headset. For more information and to test your computer, go to the Virtual Room in your UCLearn site and 'Join Course Room'. This will trigger a tutorial to help familiarise you with the functionality of the Virtual Room.

Work placement, internships or practicums

None

Next step

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