Neural Network and Deep Learning

    Unit code: NIT6004 | Study level: Postgraduate
    (Generally, 1 credit = 10 hours of classes and independent study.)
    City Campus
    Footscray Park
    Online Real Time
    VU Brisbane
    VU Sydney
    NIT5150 - Advanced Object Oriented Programming
    (Or equivalent to be determined by unit coordinator)


    In this unit, some of the most widely used regression, prediction and classification models will be covered. Neural networks will be introduced, and the backpropagation as the primary training algorithm will be demonstrated. Various forms of deep neural networks such as multilayer perceptions, convolutional neural networks, and recurrent neural networks will be examined. The mathematics of stochastic optimisation is used to explain the behaviour and training of these networks. Various programming approaches will be discussed and demonstrated for the training and deployment of neural networks. The application of deep learning technologies will be discussed in areas such as pattern recognition. Students will learn, discuss and evaluate solutions from the perspective of data privacy and professional ethics.

    Learning Outcomes

    On successful completion of this unit, students will be able to:

    1. Critically review the performance and applications of neural network and deep learning techniques;
    2. Implement a systematic approach to design and evaluate neural network architecture;
    3. Interpret relevant mathematical equations or statistical methodologies in terms of neural network architecture and deep learning methods;
    4. Investigate and apply knowledge discovery processes and associated models to innovate deep learning applications considering the importance of data privacy and professional ethics to support and provide business solutions; and
    5. Extrapolate knowledge and skills to design, develop, and evaluate a variety of deep learning tasks: modelling, clustering, dimensionality reduction, regression or classification.


    For Melbourne campuses

    Assessment type: Laboratory Work
    Grade: 20%
    Lab submissions (2)
    Assessment type: Project
    Grade: 40%
    Projects (2)
    Assessment type: Case Study
    Grade: 40%
    In-class Problem Solving Case Study

    Required reading

    Reading materials and other resources will be provided in class or through VU Collaborate.

    As part of a course

    This unit is studied as part of the following course(s):

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