Real and Artificial Neural Networks (NEUR30006)

Undergraduate level 3Points: 12.5On Campus (Parkville)

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Overview

Availability
Semester 2
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The analysis of real neural networks and the construction of artificial neural networks afford mutually synergistic technologies with broad application within and beyond neuroscience. Artificial neural networks, and other machine learning methods, have found numerous applications in analysis and modelling, and have produced insights into numerous complex phenomena (and generated huge economic value). Such technologies can also be used to gain insights into the biological systems that inspired their creation: we will explore how learning is instantiated in artificial and biological neural networks.

The subject aims to provide foundation skills for those who may wish to peruse neuroscience - or any research or work environment that involves the creation or capture, and analysis, of complex data. Students will gain experience with digital signals and digital signal processing (whether those signals are related to images, molecular data, connectomes, or electrophysiological recordings), and will learn how to conceptualise and implement approaches to modelling data by constructing an artificial neural network using the Python programming language.

Intended learning outcomes

On completion of this subject students should be able to:

  • Program in Python using Jupyter notebook.
  • Build an artificial neural network to perform a learning task on a large dataset.
  • Visualise and evaluate the performance of the artificial neural networks they construct.
  • Understand the nature of data, noise, and computational models.

Generic skills

On completion of this subject students should have developed skills in:

  • Independent critical thought.
  • Understanding different experimental approaches and modelling approaches to problems.
  • Analysing complex scientific problems and interpreting experimental findings.
  • Understanding the interrelationship of ideas and technologies in multi-disciplinary science.

Last updated: 14 March 2025

Eligibility and requirements

Prerequisites

One of

Code Name Teaching period Credit Points
NEUR30002 Neurophysiology: Neurons and Circuits
Semester 1 (On Campus - Parkville)
 12.5
NEUR30003 Principles of Neuroscience
Semester 1 (On Campus - Parkville)
 12.5

Corequisites

None

Non-allowed subjects

None

Recommended background knowledge

High School-level differential calculus and matrix operations.

Inherent requirements (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

Last updated: 14 March 2025

Assessment

DescriptionTimingPercentage

Four 15-minute quizzes in weeks 2, 4, 6 & 8. (5% each).

  • 60 minutes
Throughout the semester20%

Mid-semester test

  • 50 minutes
Mid semester20%

Written report

  • 3000 words
During the examination period60%

Last updated: 14 March 2025

Dates & times

  • Semester 2
    Principal coordinatorPeter Kitchener
    CoordinatorGer Post
    Mode of deliveryOn Campus (Parkville)
    Contact hours1-hour seminar, 1-hour tutorial and 1-hour workshop per week.
    Total time commitment170 hours
    Teaching period28 July 2025 to 26 October 2025
    Last self-enrol date 8 August 2025
    Census date 1 September 2025
    Last date to withdraw without fail26 September 2025
    Assessment period ends21 November 2025

    Semester 2 contact information

    Subject Coordinator 

    A/Prof Peter Kitchener

    p.kitchener@unimelb.edu.au 

    Mr Ger Post

    ger.post@unimelb.edu.au 

     

    Administrative Coordination

    Current Students

    Past Students, Future Students and General Enquiries

Time commitment details

36 contact hours with 134 hours of independent work on an artificial neural network.

What do these dates mean

Visit this webpage to find out about these key dates, including how they impact on:

  • Your tuition fees, academic transcript and statements.
  • And for Commonwealth Supported students, your:
       -  Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
     

    Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.

Last updated: 14 March 2025

Further information

Last updated: 14 March 2025