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  3. Statistical Machine Learning
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Statistical Machine Learning (COMP90051) // Further information

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Further information

  • Texts

    Prescribed texts

    None

  • Subject notes

    LEARNING AND TEACHING METHODS

    The subject is delivered through a combination of lectures and tutorials. One feature of the subject is that the projects are designed to be relatively open-ended and broad enough that students have scope to get hands-on experience implementing the breadth of material covered in the subject, as well as building off the subject content in innovating their own methods/researching related methods from the research literature and implementing them themselves.

    INDICATIVE KEY LEARNING RESOURCES

    Students will have access to lecture slides, readings relating to the lecture materials (both from a textbook and conference/journal papers), tutorial worksheets with worked solutions for all numeric problems, and sample reports to use in writing the project reports. Students are permitted to do their programming in any language and any programming environment/OS.

    CAREERS / INDUSTRY LINKS

    Machine learning has been growing rapidly in industry over the past two decades, with key industry players including Google, Microsoft, Amazon, Facebook and Twitter. There have been guest lecturers in the subject from organisations such as NICTA, which has a strong interest in machine learning (indeed one of the primary research groupings within NICTA is based on Machine Learning).

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    This subject contributes to the following:

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  • Available through the Community Access Program

    About the Community Access Program (CAP)

    This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.

    Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.

    Additional information for this subject

    Subject coordinator approval required

Last updated: 3 November 2022