Handbook : Unit informationEdith Cowan University

School: Science

This unit information may be updated and amended immediately prior to semester. To ensure you have the correct outline, please check it again at the beginning of semester.

• Unit Title

  Data Analysis and Visualisation

• Unit Code

  CYB6009

• Year

  2019

• Enrolment Period

  1

• Version

  1

• Credit Points

  20

• Full Year Unit

  N

• Mode of Delivery

  Online

• Unit Coordinator

  Dr Johnny Su Hau LO

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Description

This unit introduces students to the principles and practices of machine learning to uncover patterns and trends in complex data sets, and to visualise these patterns in meaningful ways. Machine learning is a process by which computer models are not explicitly programmed but "learn from data". Students will use existing data to develop models used to predict various outcomes for new data.

Non Standard Timetable Requirements

Students undertake this unit in an accelerated delivery mode over 7 weeks.

Learning Outcomes

On completion of this unit students should be able to:

1. Critically assess the strengths and weaknesses of a range of machine learning methodologies as used in a range of applications.
2. Select, implement and train appropriate machine learning algorithms for given real-world applications.
3. Objectively use a range of modern visualisation methods appropriate for different types of data.

Unit Content

1. Principles of unsupervised and supervised machine learning.
2. Model selection and feature selection.
3. Model optimisation: cost functions, search space and other methods.
4. Model evaluation and visualisation.
5. Current machine learning methods to analyse and visualise large and complex data sets.

Assessment

GS1 GRADING SCHEMA 1 Used for standard coursework units

Students please note: The marks and grades received by students on assessments may be subject to further moderation. All marks and grades are to be considered provisional until endorsed by the relevant Board of Examiners.

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