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Unit of Study AIT91001 - Computational Intelligence and Machine Learning (2018)

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Unit Snapshot

  • Pre-requisites

    • CSC72003 - Programming II
      • OR - Fundamentals of Programming unless one of these units has been approved for advanced standing.

Unit description

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

 

Availabilities

Not currently available in 2018

Learning outcomes and graduate attributes

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:

GA1: , GA2: , GA3: , GA4: , GA5: , GA6: , GA7:
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: