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Gold Coast
Online

Unit Summary

Unit type

PG Coursework Unit

Credit points

12

AQF level

9

Level of learning

Introductory

Unit aim

Introduces students to machine learning. Students will take an algorithmic approach to machine learning in which real-world problems will be solved through machine learning techniques. Students will familiarise themselves with a wide range of algorithms and implement them for problem solving in Python/Octave.

Unit content

Topic 1: Intro to machine learning

Topic 2: Types of machine learning

Topic 3: Regression and prediction

Topic 4: Classification

Topic 5: Neural networks

Topic 6: Machine learning: best practices

Topic 7: Philosophy of machine learning

 

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.

GA1: , GA2: , GA3: , GA4: , GA5: , GA6: , GA7:
On completion of this unit, students should be able to: GA1 GA2 GA3 GA4 GA5 GA6 GA7
1 identify and evaluate the evolution and recent trends in computational intelligence and machine learning
2 critically analyse real-world machine learning problems and compare between a range of solutions in a variety of contexts
3 develop, create and implement machine learning solutions to complicated problems
4 plan and implement parts of the tasks in a machine learning pipeline to complete a predictive analysis problem to satisfaction

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

  1. identify and evaluate the evolution and recent trends in computational intelligence and machine learning
    • GA4:
    • GA5:
  2. critically analyse real-world machine learning problems and compare between a range of solutions in a variety of contexts
    • GA2:
    • GA4:
    • GA5:
  3. develop, create and implement machine learning solutions to complicated problems
    • GA2:
    • GA4:
  4. plan and implement parts of the tasks in a machine learning pipeline to complete a predictive analysis problem to satisfaction
    • GA2:
    • GA4:

Prescribed texts

  • Prescribed text information is not currently available.

  • 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.