• Code COMP8604
  • Unit Value 6 to 12 units
  • Offered by School of Computing
  • ANU College ANU College of Engineering and Computer Science
  • Course subject Computer Science
  • Areas of interest Computer Science
  • Academic career PGRD
  • Course convener
    • Dr Sid Chi-Kin Chau
  • Mode of delivery In Person
  • Offered in First Semester 2022
    Second Semester 2022
    See Future Offerings

This is a flexible 6 or 12-unit course which allows students to undertake a substantial research project on a machine learning and/or computer vision and/or Artificial Intelligence topic. Students will be expected to carry out independent research on this topic whilst working under a supervisor of an academic or a qualified researcher. The research project can be theoretical, experimental, design or development based on the interests of the student and the availability of supervisors. Project overview and assessment arrangements will be specified at the outset using the School of Computing form 'Independent Study Contract' where applicable. The output of the research project should include a written report and maybe examinable source code.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Formulate and clearly define a research question including appropriate scope, aims and constraints, and identify a suitable methodology to address the problem.
  2. Apply research skills and methodologies to source, critically evaluate, and synthesise research and technical literature relevant to the project.
  3. Apply advanced theory, concepts and technical skills to design and conduct experimental, theoretical or computational research tasks.
  4. Interpret the results and justify the conclusions.
  5. Communicate the context, motivation, methods, results and conclusions of the project in written and/or oral form, using language and content appropriate to the intended audience.

Indicative Assessment

  1. To be determined by the convener (100) [LO 1,2,3,4,5]

The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.

Workload

The students should meet the supervisors as often as necessary. Nominally 20 hours per week.

Inherent Requirements

Not applicable

Requisite and Incompatibility

To enrol in this course you must be enrolled in the Master of Machine Learning and Computer Vision and have completed 24 units of the MMLCV program. You also need to have a signed 'Independent Study contract' with a supervisor before the start of the semester in which you plan to start your project.

You will need to contact the School of Computing to request a permission code to enrol in this course.

Prescribed Texts

none

Fees

Tuition fees are for the academic year indicated at the top of the page.  

Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees

Student Contribution Band:
2
Unit value:
6 to 12 units

If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.

Where there is a unit range displayed for this course, not all unit options below may be available.

Units EFTSL
6.00 0.12500
7.00 0.14583
8.00 0.16667
9.00 0.18750
10.00 0.20833
11.00 0.22917
12.00 0.25000
Domestic fee paying students
Year Fee
2022 $790 per unit
International fee paying students
Year Fee
2022 $1000 per unit
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

The list of offerings for future years is indicative only.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.

First Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
4549 21 Feb 2022 28 Feb 2022 31 Mar 2022 27 May 2022 In Person View

Second Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
7584 25 Jul 2022 01 Aug 2022 31 Aug 2022 28 Oct 2022 In Person N/A

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