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Machine Learning (COMP30027)

Undergraduate level 3Points: 12.5On Campus (Parkville)

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Overview

Year of offer2019
Subject levelUndergraduate Level 3
Subject codeCOMP30027
Campus
Parkville
Availability
Semester 1
FeesSubject EFTSL, Level, Discipline & Census Date

AIMS

Machine Learning, a core discipline in data science, is prevalent across Science, Technology, the Social Sciences, and Medicine; it drives many of the products we use daily such as banner ad selection, email spam filtering, and social media newsfeeds. Machine Learning is concerned with making accurate, computationally efficient, interpretable and robust inferences from data. Originally borne out of Artificial Intelligence, Machine Learning has historically been the first to explore more complex prediction models and to emphasise computation, while in the past two decades Machine Learning has grown closer to Statistics gaining firm theoretical footing.

This subject aims to introduce undergraduate students to the intellectual foundations of machine learning, and to introduce practical skills in data analysis that can be applied in graduates' professional careers.

CONTENT

Topics will be selected from: prediction approaches for classification/regression such as k-nearest neighbour, naïve Bayes, discriminative linear models, decision trees, Support Vector Machines, Neural Networks; clustering methods such as k-means, hierarchical clustering; probabilistic approaches; exposure to large-scale learning.

Intended learning outcomes

INTENDED LEARNING OUTCOMES (ILO)

On completion of this subject the student is expected to:

  1. Recognise real-world problems as amenable to machine learning
  2. Apply machine learning algorithms and end-to-end statistical processes correctly
  3. Interpret the results of machine learning run on real data
  4. Compare benefits/drawbacks of competing models and algorithms, relevant to real problems
  5. Derive machine learning algorithms from statistical first principles.

Generic skills

On completion of this subject the student is expected to possess:

  1. Ability at logical problem solving: undertake problem identification, formulation and solution
  2. Capacity for creativity and innovation
  3. Ability to communicate effectively within both the engineering team and the community at large
  4. Profound respect for truth and intellectual integrity, and for the ethics of scholarship
  5. An expectation of the need to undertake lifelong learning, and the capacity to do so.

Eligibility and requirements

Prerequisites

Both of:

Code Name Teaching period Credit Points
COMP20008 Elements of Data Processing
Semester 1
Semester 2
12.5
COMP10002 Foundations of Algorithms
Semester 1
Semester 2
12.5

Corequisites

None

Non-allowed subjects

None

Recommended background knowledge

Basic probability theory, equivalent to material covered in Victorian Certificate of Education (VCE) Mathematical Methods 3/4.

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

Additional details

  • Group-work project 1 – Programming project (ILO2,5), requiring approximately 30 hours of work, due in weeks 5-6 (20%)
  • Group-work project 2 – Prediction competition (ILO1-4, requiring approximately 30 hours of work, due in weeks 9-10 (20%)
  • Final 2 hours, closed book examination (ILO1-5), during the examination period (60%)

Dates & times

  • Semester 1
    CoordinatorJeremy Nicholson
    Mode of deliveryOn Campus — Parkville
    Contact hours48 hours total; two 1 hour lectures, one 1 hour practice class, one 1 hour tutorial, weekly.
    Total time commitment170 hours
    Teaching period 4 March 2019 to 2 June 2019
    Last self-enrol date15 March 2019
    Census date31 March 2019
    Last date to withdraw without fail10 May 2019
    Assessment period ends28 June 2019

    Semester 1 contact information

    Jeremy Nicholson

    nj@unimelb.edu.au

Time commitment details

170 hours.

Further information

  • Texts

    Prescribed texts

    None

  • Breadth options

    This subject is available as breadth in the following courses:

  • 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/or Study Exchange Students

    This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.

Last updated: 25 July 2019