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MA3832 - Neural Network and Deep Learning

Credit points: 3
Year: 2021
Student Contribution Band: Band 1
Administered by: College of Science and Engineering

This subject in a continuation from MA3405/MA5405. In this subject, students will learn the mechanics of modern neural networks, essential skill in cloud comping and deploy the model onto cloud platform. The concepts of regularisation, pooling, optimisation momentum and convolution are introduced and applied in the field of computer vision neural networks. In this subject, Amazon is used to apply machine learning optimisation to machine learning tasks. This methodology of using machine learning for optimisation greatly enhances the outcomes of model development and removes some of the intuition needed for model development. At the conclusion of the subject, students will provide a detailed computer vision project plan using industry standard planning method.

Learning Outcomes

  • demonstrate and apply advanced theoretical and technical knowledge of neural network to an industry or research problem;
  • develop multi-layer perceptron and convolution neural networks;
  • programmatically interact with AWS using Python Jupyter Notebooks;
  • develop and deploy machine learning models on AWS.

Subject Assessment

  • Written > Essay (including multi-draft) 1 - (25%) - Individual
  • Written > Case study analysis - (25%) - Individual
  • Written > Project report - (50%) - Individual.
Prerequisites: MA3405 OR MA5405 AND CP1404

Availabilities

Cairns , Internal, Study Period 2
Census Date 26-Aug-2021
Lecturer: Dr Kelly Trinh.
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 26 hours lectures (didactic or interactive) - May include pre-recorded content
  • 26 hours workshops - May include online content
  • assessment and self-directed study

Townsville, Internal, Study Period 2
Census Date 26-Aug-2021
Lecturer: Dr Kelly Trinh.
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 26 hours lectures (didactic or interactive) - May include pre-recorded content
  • 26 hours workshops - May include online content
  • assessment and self-directed study

Note: Minor variations might occur due to the continuous Subject quality improvement process, and in case of minor variation(s) in assessment details, the Subject Outline represents the latest official information.