1. Handbook
  2. Subjects
  3. Statistical Machine Learning
  4. Print

Statistical Machine Learning (COMP90051)

Graduate courseworkPoints: 12.5On Campus (Parkville)

You’re viewing the 2018 Handbook:
Or view archived Handbooks
You’re currently viewing the 2018 version of this subject

Overview

Year of offer2018
Subject levelGraduate coursework
Subject codeCOMP90051
Campus
Parkville
Availability
Semester 2
FeesSubject EFTSL, Level, Discipline & Census Date

AIMS

With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events.

Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks.

This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems.

INDICATIVE CONTENT

Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods.

Intended learning outcomes

INTENDED LEARNING OUTCOMES (ILO)

On completion of this subject the student is expected to:

  1. Describe a range of machine learning algorithms
  2. Design, implement and evaluate learning systems to solve real-world problems, based on an appreciation of their relative suitability to different tasks

Generic skills

On completion of the subject students should have the following skills:

  • Ability to undertake problem identification, formulation, and solution
  • Ability to utilise a systems approach to complex problems and to design and operational performance ability to manage information and documentation
  • Capacity for creativity and innovation
  • Ability to communicate effectively both with the engineering team and the community at large.

Eligibility and requirements

Prerequisites

One of the following:

Code Name Teaching period Credit Points
COMP30018 Knowledge Technologies 12.5
COMP90049 Knowledge Technologies
Semester 1
Semester 2
12.5
COMP30027 Machine Learning
Semester 1
12.5

OR

admission into MCIT-100 program.

Corequisites

None

Non-allowed subjects

433-484 Machine Learning
433-679 Evolutionary and Neural Computation
433-680 Machine Learning
433-684 Machine Learning

Recommended background knowledge

Basic probability

Core participation requirements

The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.

Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home

Assessment

Description

  • Two projects due around weeks 7 and 11, requiring approximately 65 - 70 hours of work in total (50%)
  • An end-of-semester examination not exceeding 3 hours (50%).

Hurdle requirement: To pass the subject, students must obtain:

  • A mark of at least 25/50 on the exam
  • and also a combined mark of at least 25/50 for the projects.

Assessment for this subject addresses both Intended Learning Outcomes (ILOs)

Dates & times

  • Semester 2
    Principal coordinatorTrevor Cohn
    Mode of deliveryOn Campus — Parkville
    Contact hours36 hours, comprising of two 1-hour lectures and one 1-hour workshop per week
    Total time commitment200 hours
    Teaching period23 July 2018 to 21 October 2018
    Last self-enrol date 3 August 2018
    Census date31 August 2018
    Last date to withdraw without fail21 September 2018
    Assessment period ends16 November 2018

    Semester 2 contact information

    Dr Benjamin Rubinstein

    email: benjamin.rubinstein@unimelb.edu.au

Time commitment details

200 hours

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).

  • 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

  • Available to Study Abroad and Exchange students

    This subject is available to students studying at the University from overseas institutions on exchange and study abroad.

Last updated: 13 December 2018