Notice
As part of Phase 3 of our return to campus plan, most units will now run tutorials, seminars and other small group learning activities on campus for the second half-year, while keeping an online version available for those students unable to return or those who choose to continue their studies online.
To check the availability of face to face activities for your unit, please go to timetable viewer. To check detailed information on unit assessments visit your unit's iLearn space or consult your unit convenor.
Unit convenor and teaching staff |
Unit convenor and teaching staff
Convenor and Lecturer
Abhaya Nayak
Contact via Email
BD Building, Level 3, Office 357
Fri 12:00 - 13:00 (or by appointment)
Lecturer
Rolf Schwitter
Contact via Email
BD Building, Level 3, Office 359
Wed 15:00 - 16:00
Tutor
Abdus Salam
Contact via Email
See HELP101 schedule/by appointment
Tutor
Fred Amouzgar
Contact via Email
See HELP101 schedule/by appointment
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Credit points |
Credit points
10
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Prerequisites |
Prerequisites
130cp at 1000 level or above including COMP2000 or COMP229 or COMP2010 or COMP225 or COMP2110 or COMP249 or COMP2160
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Corequisites |
Corequisites
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Co-badged status |
Co-badged status
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Unit description |
Unit description
Artificial Intelligence (AI) is a well-established field that studies how computers and computer software capable of exhibiting intelligent behaviour can be designed. In this unit students will be exposed to fundamental concepts in AI such as agent architecture, knowledge representation, planning and search, as well as their application in some topical domains. Upon completion of this unit students will be able to apply problem-solving strategies that are required to build intelligent systems.
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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 one diagnostic test, two assignments and a final exam. The diagnostic test will be carried out online in iLearn. You will submit the solutions to the two assignments via iLearn by the due date. The form and date of the final examination will be announced later in the semester.
Late Submission
No extensions will be granted without an approved application for Special Consideration. There will be a deduction of 10% of the total available marks made from the total awarded mark for each 24 hour period or part thereof that the submission of the assignment is late. For example, 25 hours late in submission for an assignment worth 10 marks – 20% penalty or 2 marks deducted from the total. No submission will be accepted after solutions have been posted.
Supplementary Exam
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.
COMP3160 will be assessed and graded according to the University assessment and grading policies.
The following general standards of achievement will be used to assess each of the assessment tasks with respect to the letter grades.
Pass: Can correctly reproduce facts and definitions across a breadth of concepts, but lacks depth of understanding. Can describe and/or employ search techniques in ways that are close to those discussed in lectures or notes. Can employ AI techniques to build a basic learning machine. Has basic understanding of biologically inspired algorithms and adversarial games. Has demonstrated some ability for sound reasoning in an uncertain domain.
Credit/Distinction: As for Pass plus: Exhibits breadth and depth of understanding of concepts. Can proficiently describe and/or employ search techniques going beyond how they were discussed in lectures or notes. Can employ AI techniques to a build very good learning machine. Has excellent understanding of biologically inspired algorithms and adversarial games, and can easily employ the former to develop strategies for the latter. Has excellent understanding of the role uncertainty plays in AI and demonstrated excellence for sound reasoning in uncertain domains.
High Distinction: As for Credit/Distinction plus: Is aware of the context in which the concepts are developed and their limitations. Can cogently describe in their own words and efficiently employ search techniques, going well beyond how they were discussed in lectures or notes. Can employ AI techniques to build an excellent learning machine. Has outstanding understanding of biologically inspired algorithms and adversarial games, and can easily employ the former to develop and evaluate strategies for the latter. Has excellent understanding of the role uncertainty plays in AI and has outstanding ability for sound reasoning in uncertain domains.
Assessment Process
These assessment standards will be used to give a numeric mark to each assessment submission during marking. The mark will correspond to an appropriate letter grade when relevantly weighted. The final mark for the unit will be calculated by combining the marks for all assessment tasks according to the percentage weightings shown in the assessment summary.
Name | Weighting | Hurdle | Due |
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Diagnostic Test | 5% | No | Week 4 |
Assignment 1 | 20% | No | Week 8 |
Assignment 2 | 20% | No | Week 13 |
Final Examination | 55% | No | TBA |
Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 5 hours
Due: Week 4
Weighting: 5%
This diagnostic test will give early feedback on students' understanding of basic AI concepts (in particular search) and Python programming skills (#1).
Assessment Type 1: Programming Task
Indicative Time on Task 2: 15 hours
Due: Week 8
Weighting: 20%
The first assignment will require students to demonstrate their skills in employing their knowledge of machine learning, and programming in Python.
Assessment Type 1: Programming Task
Indicative Time on Task 2: 20 hours
Due: Week 13
Weighting: 20%
This assignment will require students to demonstrate their skills in employing their knowledge of biologically inspired algorithms to develop strategies for adversarial games (#3), and programming in Python (#1).
Assessment Type 1: Examination
Indicative Time on Task 2: 35 hours
Due: TBA
Weighting: 55%
The final examination will assess all the four learning outcomes. With regards to learning outcomes #1, #2 and #3, it allows to accurately assess the appreciation of good programming and problem solving skills. With regards to learning outcome #2, #3 and #4, it will assess students' understanding of fundamental concepts such as different types of search, games and inferences.
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 you should attend three hours of lectures, a tutorial class and a practical session. For details of days, times and rooms consult the timetables webpage. Students are urged to actively participate in the tutorials; this helps enhancing the understanding by students.
Note that practicals and tutorials commence in week 2. You should have selected a practical session and a tutorial session during enrolment. You should attend the sessions you are enrolled in.
There is no set textbook for the unit. The following are recommended readings. Lecturers may recommend other references.
S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach, Prentice-Hall, 2009.
Poole, D. and Mackworth, AK. Artificial Intelligence - Foundations of Computational Agents. Cambridge University Press 2017. (Available free of charge at: https://artint.info/2e/html/ArtInt2e.html under a Creative Commons Attribution-Noncommercial-No Derivative Works 2.5 Canada License.)
For some parts of learning, the necessary reading (book chapters, software documentation, papers, etc.) will be made available on iLearn.
COMP3160 uses iLearn for delivery of class materials, discussion boards, submission of assessment tasks and access to marks and comments. Students should check the iLearn site regularly for unit updates.
Questions that are of are of potential interest to other students in this unit, such as queries regarding the content of this unit, its tutorials or practicals, should be posted on discussion forum on iLearn.
The practical work in this unit mostly involves programming in Python3, and will require some packages purpose packages relevant to AI. Instructions will be provided on how to use Python3 and these packages on the laboratory machines and how to download them for use on your own machines
Week | Topic | Reading Material |
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1 | Unit Organisation and Introduction to AI | Lecturer Supplied |
2-4 | Search in AI | Lecturer Supplied |
5-6 | Supervised Machine Learning | Lecturer Supplied |
7-8 | Evolutionary Algorithms | Lecturer Supplied |
9-10 | Adversarial Games and Multi-Agent Systems | Lecturer Supplied |
11-12 | Uncertainty in AI | Lecturer Supplied |
13 | Revision |
Macquarie University policies and procedures are accessible from Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-central). Students should be aware of the following policies in particular with regard to Learning and Teaching:
Students seeking more policy resources can visit the Student Policy Gateway (https://students.mq.edu.au/support/study/student-policy-gateway). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
If you would like to see all the policies relevant to Learning and Teaching visit Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-central).
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/study/getting-started/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 ask.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au
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Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to help you improve your marks and take control of your study.
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