Notice
As part of Phase 3 of our return to campus plan, most units will now run tutorials, seminars and other small group activities on campus, and most will keep an online version available to those students unable to return or those who choose to continue their studies online.
To check the availability of face-to-face and online 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
Annabelle McIver
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Credit points |
Credit points
10
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Prerequisites |
Prerequisites
Admission to MRes
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Corequisites |
Corequisites
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Co-badged status |
Co-badged status
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Unit description |
Unit description
Algorithms are the essence of computer science. In this unit we build on the undergraduate understanding of algorithms and look at interesting and useful algorithms, both fundamental and cutting edge. The particular material covered will depend on the cohort but may include topics such as approximation algorithms, exponential-time exact and parameterized algorithms, linear and constraint programming and fundamental graph algorithms such as max-flow algorithms, matching algorithms an so on. The unit will also employ appropriate tools from complexity theory to analyse the performance of the algorithms studied.
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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:
COMP7000 will be assessed and graded according to the University assessment and grading policies.
Assessment deadlines are strict, unless an application for special consideration is received (preferably in advance) accompanied by appropriate documentary evidence. Late submissions will be penalised at the rate of 20% of the full marks for the assessment per day or part thereof late.
The following general standards of achievement will be used to assess each of the assessment tasks with respect to the letter grades.
Pass: Has a basic understanding of the algorithms and concepts as discussed in class. Can describe and reproduce definitions and fundamental algorithms. Can perform a basic research investigation in the area and present the results of that research in rudimentary written and oral forms.
Credit: As for Pass plus: Is able to apply the algorithmic techniques we have discussed to derive solutions to computational problems. Can develop, generalise and apply the concepts discussed in class to address basic theoretical and practical questions, and can effectively communicate these insights. Shows more than basic insights into the results of a research investigation and is able to communicate those insights.
Distinction/High Distinction: As for Credit plus: Is able to generalise and synthesise knowledge to address more complex topics beyond the material discussed in class. Can critically evaluate the limits of the techniques and algorithms discussed.
These assessment standards will be used to give a numeric mark out of 100 to each assessment submission during marking. The mark will correspond to a letter grade for that task according to the University guidelines. The final raw 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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Weekly tasks | 36% | No | Weeks 1 -- 12 |
Projects | 54% | No | Weeks 3 -- 12 |
Presentation | 10% | No | Weeks 12--13 |
Assessment Type 1: Problem set
Indicative Time on Task 2: 0 hours
Due: Weeks 1 -- 12
Weighting: 36%
Each week students will be asked to complete some exercises to test their understanding of the material.
Assessment Type 1: Project
Indicative Time on Task 2: 40 hours
Due: Weeks 3 -- 12
Weighting: 54%
Students will be asked to complete 4 projects. These will consist of a combination of programming, program analysis and report writing.
Assessment Type 1: Presentation
Indicative Time on Task 2: 10 hours
Due: Weeks 12--13
Weighting: 10%
An oral presentation supported by appropriate presentation materials.
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 face-to-face class. These classes will be a mixture lecture material, discussion and in class tests.
There is no set text for the course, but the following far from exhaustive list of texts may be useful for reference, study and further reading:
The materials for the unit including notes, discussion fora, electronic submission links etc. will be through the iLearn system.
The programming projects can be done in any programming language subject to prior approval of the course convener. Languages can include Java and Python.
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 ask.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
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.
The Library provides online and face to face support to help you find and use relevant information resources.
Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.
For all student enquiries, visit Student Connect at ask.mq.edu.au
If you are a Global MBA student contact globalmba.support@mq.edu.au
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.
Unit information based on version 2021.02 of the Handbook