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LocationDomesticInternational
Gold Coast
Online

Unit description

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

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 Learning Resources may change in future Teaching Periods

Teaching and assessment

Teaching method
Workshop 1 hour (weekly)
Tutorial 2 hours (weekly)
Assessment
Practical skills30%
Practical skills60%
Short written response10%

Teaching method
Workshop 1 hour (weekly)
Tutorial 2 hours (weekly)
Assessment
Practical skills30%
Practical skills60%
Short written response10%
Notice

Intensive offerings may or may not be scheduled in every teaching period. Please refer to the timetable for further details.

Southern Cross University employs different teaching methods within units to provide students with the flexibility to choose the mode of learning that best suits them. SCU academics strive to use the latest approaches and, as a result, the learning modes and materials may change. The most current information regarding a unit will be provided to enrolled students at the beginning of the teaching period.

Fee information

Domestic

Commonwealth Supported courses
For information regarding Student Contribution Amounts please visit the Student Contribution Amounts.

Fee paying courses
For postgraduate or undergraduate full fee paying courses please check Domestic Postgraduate Fees OR Domestic Undergraduate Fees

International

Please check the international course and fee list to determine the relevant fees.

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