The course introduces students to some fundamentals of research methodology. The course comprises a series of lectures which cover the following topics: Philosophy of Science, Quantitative Research Methods, Qualitative Research Methods, Basic Machine Learning Methods, Theoretical Research Methods, How to find a Research Topic, Literature Analysis, Reading and Reviewing Papers, Research Ethics and Commercialising Research.The lectures will be complemented by a series of workshops, labs and assignments that require students to do some small research-focused tasks that help them get a hands-on experience of research, both individually and in teams of students. This includes different tasks such as topic modelling, statistical analysis, applying different machine learning techniques to solve a problem in a team, proving theorems, complexity analysis, designing a research project, reviewing papers and presenting papers.Workshops cover topics such as research integrity, time management and project management, teamwork, reading strategies, report writing, and presenting research. At the end of the course, students will be confident to start working on their own research projects.
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Requisite and Incompatibility
To enrol in this course you must be enrolled in Master of Computing (Advanced) (VCOMP) or Master of Machine Learning and Computer Vision (MMLCV). Incompatible with COMP2550 and COMP4450
Referenced entries (2)
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- COMP2550 · 2027 · saved source not linked
- COMP4450 · 2027 · saved source not linked
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
Effectively communicate research questions, methods and outcomes in oral, written and graphical forms to a broad audience using appropriate tools.
Justify and apply appropriate ethical technological choices based on an advanced consideration of the research question and methods.
Explain the process of identifying, composing and formulating ethical research questions.
Apply effective literature search and critical evaluation skills.
Explain the role of academic integrity and the importance of building upon and correctly attributing the work of others.
Published offerings
Future offerings are indicative. Only recognised 2027 sessions with a published class number are offered in the enrolment demo.
| Year | Session | Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|---|---|
| 2027 | First Semester | 5082 | 22 Feb 2027 | 01 Mar 2027 | 31 Mar 2027 | 28 May 2027 | In Person | N/A |
| 2028 | First Semester | 6487 | 21 Feb 2028 | 28 Feb 2028 | 31 Mar 2028 | 26 May 2028 | In Person | N/A |
Complete offering text
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage. 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. 2027 2028 First Semester Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary 5082 22 Feb 2027 01 Mar 2027 31 Mar 2027 28 May 2027 In Person N/A First Semester Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary 6487 21 Feb 2028 28 Feb 2028 31 Mar 2028 26 May 2028 In Person N/A
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