Artificial Intelligence Techniques PG (6685.5)
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) |
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 effectively1. 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 PGCorequisites
11521 Programming for Data Science G OR 8936 Introduction to Information Technology GIncompatible units
None.Equivalent units
None.Assumed knowledge
None.Year | Location | Teaching period | Teaching start date | Delivery mode | Unit convener |
---|---|---|---|---|---|
2024 | Bruce, Canberra | Semester 1 | 05 February 2024 | On-campus | Dr Ram Subramanian |
2025 | Bruce, Canberra | Semester 1 | 03 February 2025 | On-campus | Dr Ram Subramanian |
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
In order to pass the unit, a student needs to satisfy both Criterion 1 and Criterion 2.
(1) Criterion 1:The final mark for this unit will be calculated as the sum of scores achieved over all weighted assessments. To pass the unit the student must score a minimum aggregate of 50%.
(2) Criterion 2: A total of 8 lab exercises will be provided for students' practice, and to facilitate their understanding of concepts. At least 4 of these lab exercises need to be completed and returned by the students in order to pass the unit.
(3) The unit convenor reserves the right to question students on any of their submitted work for moderation and academic integrity purposes, which may result in an adjustment to the marks awarded for a specific task.
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 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 Mid-sem (Report & Presentation) |
32 |
Project End-sem (Report & Presentation) |
40 |
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 either Matlab or 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