Bayesian Models

Monash University’s Faculty of Information Technology has a clear set of information and instructions for candidate research students including requirements, the application process, etc.  Follow the menu tabs on the left after you have finished looking at requirements.  There is a pre-screening process to save everyone time too.

For my part, I like to see the following in a research candidate:

• good capability in mathematics (statistics, real analysis, matrices and vectors)
• good capability in coding (know how to develop a small system in a language of their choice)
• familiarity with machine learning
• if your mathematics or coding is excellent then you don’t need all the above

Monash FIT keeps a list of candidate PhD projects, though good supervisors usually have another 3-4 quality PhD topics sitting around for those who would like some other ideas.

I’m happy to let students look around in their first year because I believe its important that you are completely motivated on your thesis.  Note, also, Australian scholarships are usually attached to the student, not the professor.

Research is often better when you work in a multi-disciplinary way.  So I encourage partnering with other supervisers both within and outside Machine Learning at Monash.  Our group’s webpage is here.  Happy, especially, to work with NLP and IR folks at Monash, Uni. of Melbourne and RMIT.

So if you’ve read all the way to the end here, then the next step is to email me (contact details here) with a brief introduction.  First thing I’ll ask you for is a copy of your transcripts and your CV.

In the meantime, check out these resources for research students and some tutorial resources for researchers as well as these great (but old) tutorials.