Unit convenor and teaching staff |
Unit convenor and teaching staff
Convenor, Lecturer
Mehmet Orgun
Contact via email
4RPD, 282
Lecturer
Rolf Schwitter
Contact via email
4RPD, 359
|
---|---|
Credit points |
Credit points
10
|
Prerequisites |
Prerequisites
COMP6400
|
Corequisites |
Corequisites
|
Co-badged status |
Co-badged status
|
Unit description |
Unit description
The fast moving field of Artificial Intelligence (AI) continues to push the frontiers what machines can achieve. This unit surveys emerging topics and trends in AI. These topics drawn from the latest research literature vary from offering to offering, their selection being inspired by cutting-edge development in the field. These topics include but are not limited to: decision making under uncertainty, reasoning, planning, machine learning, natural language understanding and the legal and ethical implications of AI-driven technologies. The unit consists of lectures, reading, and assessed components of scientific writing in various forms. Learning in this unit enhances student understanding of global challenges identified by the United Nations Sustainable Development Goals (UNSDGs) Industry, Innovation and Infrastructure |
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
The assessment of this unit consists of two individual assignments and a final exam. The form and date of the final examination will be announced later in the semester.
Assignment 1: To be released no later than 29 August, 2025.
Assignment 2: To be released no later than 10 October, 2025.
To pass this unit, you must achieve a total mark equal to or greater than 50%.
From 1 July 2022, Students enrolled in Session based units with written assessments will have the following late penalty applied. Please see https://students.mq.edu.au/study/assessment-exams/assessments for more information.
Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark) will be applied each day a written assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of '0' will be awarded even if the assessment is submitted.
Submission time for all written assessments is set at 11:55 pm on the date they are due. A 1-hour grace period is provided to students who experience a technical concern.
For any late submission of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, students need to submit an application for Special Consideration.
In this unit, late submissions will be accepted as follows:
In general, if you receive Special Consideration for the final exam, a supplementary exam will be scheduled after the normal exam period, following the release of marks. By making a special consideration application for the final exam you are declaring yourself available for a resit during the supplementary examination period and will not be eligible for a second special consideration approval based on pre-existing commitments. Please ensure you are familiar with the policy prior to submitting an application. Approved applicants will receive an individual notification one week prior to the exam with the exact date and time of their supplementary examination.
This unit will be assessed and graded according to the University assessment and grading policies. There is no hurdle assessment in this unit. The final grade is determined by the total mark the student obtains in all the assessment tasks they completed as follows:
The Special Consideration Policy aims to support students who have been impacted by short-term circumstances or events that are serious, unavoidable and significantly disruptive, and which may affect their performance in assessment. If you experience circumstances or events that affect your ability to complete the assessments in this unit on time, please inform the convenor and submit a Special Consideration request through Service Connect.
Name | Weighting | Hurdle | Due |
---|---|---|---|
Assignment 1 | 30% | No | 19/09/2025 |
Assignment 2 | 30% | No | 31/10/2025 |
Final Examination | 40% | No | On-campus written exam |
Assessment Type 1: Media presentation
Indicative Time on Task 2: 40 hours
Due: 19/09/2025
Weighting: 30%
The students will evaluate two existing the state-of-the-art artificial intelligence systems on worked scenarios; and produce a short written report as well as a video presentation.
Assessment Type 1: Case study/analysis
Indicative Time on Task 2: 40 hours
Due: 31/10/2025
Weighting: 30%
Students will conduct a case study of an AI application in an industry context, identify problems, develop a strategy, assess ethical and legal implications, and present findings in a written report.
Assessment Type 1: Examination
Indicative Time on Task 2: 20 hours
Due: On-campus written exam
Weighting: 40%
Demonstrate an understanding of a selection of topics covered in the unit.
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
Each week has two hours of lectures. There is no workshop/practical class for this unit.
For details of days, times and rooms of the lectures, please consult the Classfinder tool in eStudent (read about using the Classfinder tool for more information).
All required and recommended readings will be provided as part of the lecture material.
The unit web page will be hosted in iLearn. You will need to log in to iLearn using your Student One ID and password. The unit will make extensive use of discussion boards also hosted in iLearn. Please post questions there, as they will be monitored by the staff on the unit.
We will communicate with you via your university email or through announcements in iLearn. Questions to convenors can either be placed on the iLearn discussion board or sent to the unit convenor from your university email address.
Unit Schedule
Week | Topic | Reading |
1 |
+ Towards Statistical Relational Artificial Intelligence + Imperative versus Declarative Programming |
Lecturer Supplied |
2 |
+ Answer Set Programming + Optimisation in Answer Set Programming |
Lecturer Supplied |
3 |
+ Commensense Knowledge and Reasoning + Diagnosis and Explanations |
Lecturer Supplied |
4 |
+ Probabilistic Logic Programs (PLPs) + Inference Tasks for PLPs |
Lecturer Supplied |
5 |
+ Parameter Learning of PLPs + Structure Learning of PLPs |
Lecturer Supplied |
6 |
+ PLPs for Natural Language Understanding + Neural Probabilistic Logic Programming |
Lecturer Supplied |
7 |
+ Introduction to Model Checking + Abstract Models and Specifications of Systems |
Lecturer Supplied |
8 |
+ Linear Temporal Logic (LTL) + LTL Equivalences |
Lecturer Supplied |
RECESS |
||
9 |
+ The NuSMV Model Checker + Practical Model Checking Examples |
Lecturer Supplied |
10 |
+ Computation Tree Logic (CTL) + Model Checking CTL formulas |
Lecturer Supplied |
11 |
+ Model Checking LTL formulas + From LTL formulas to automata |
Lecturer Supplied |
12 |
+ Binary Decision Diagrams (BDDs) + Operations on BDDs |
Lecturer Supplied |
13 |
+ Review: First Half of the Unit + Review: Second Half of the Unit |
Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:
Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct
Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit connect.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au
At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
Academic Success provides resources to develop your English language proficiency, academic writing, and communication skills.
The Library provides online and face to face support to help you find and use relevant information resources.
Macquarie University offers a range of Student Support Services including:
Got a question? Ask us via the Service Connect Portal, or contact Service Connect.
For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/.
When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.
Compared with the previous offering in 2024, the assessment structure in this unit has been revised to comply with the 3 assessment policy. The two assignments are retained with the same workload and weights, however, the weekly portfolio task assessment has been replaced with a final exam, worth 40%.
This is the second time this unit will be offered. As before, we always value student feedback to be able to continually improve the way we offer our units. As such we encourage students to provide constructive feedback via student surveys, to the teaching staff directly, or via the FSE Student Experience & Feedback link in the iLearn page.
Unit information based on version 2025.03 of the Handbook