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Gold Coast
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Unit Summary

Unit type

PG Coursework Unit

Credit points

12

Unit aim

Introduces students to computational intelligence and machine learning techniques including regression and artificial neural networks. The unit provides students with foundation knowledge and skills to utilize a range of computational intelligence and machine learning algorithms. Students will take an algorithmic approach to machine learning and learn through solving real-world problems.

Unit content

Module 1: Introduction to machine learning
Module 2: Regression modelling
Module 3: Generalisation, model assessment and selection
Module 4: Feed-forward neural networks and backpropagation
Module 5: Convolutional neural networks
Module 6: Model regularisation

Learning outcomes

Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes. The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.

On completion of this unit, students should be able to:
1 analyse and select machine learning solutions for real-world problems.
2 design and develop various machine learning based models.
3 evaluate and enhance the models to meet requirements.
4 interpret and communicate the results to stakeholders.

On completion of this unit, students should be able to:

  1. analyse and select machine learning solutions for real-world problems.
  2. design and develop various machine learning based models.
  3. evaluate and enhance the models to meet requirements.
  4. interpret and communicate the results to stakeholders.

Prescribed texts

  • Geron, A, 2019, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : concepts, tools, and techniques to build intelligent systems , 2nd edn , O'Reilly.
Prescribed texts may change in future teaching periods.