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.

Please note that given the circumstances of COVID-19, there may be some modifications to the assessment schedule promoted in Handbook for Semester 1 2020 Units. Students will be notified of all approved modifications by Unit Coordinators via email and Unit Blackboard sites. Where changes have been made, these are designed to ensure that you still meet the unit learning outcomes in the context of our adjusted teaching and learning arrangements.

• Unit Title

  Data Analysis and Visualisation

• Unit Code

  MAT3170

• Year

  2020

• Enrolment Period

  1

• Version

  1

• Credit Points

  15

• Full Year Unit

  N

• Mode of Delivery

  On Campus
  Online

• Unit Coordinator

  Dr Johnny Su Hau LO

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Description

This unit introduces students to the basic 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.

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. 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.

Learning Experience

ON-CAMPUS

Students will attend on campus classes as well as engage in learning activities through ECU Blackboard.

For more information see the Semester Timetable

ONLINE

Students will engage in learning experiences through ECU Blackboard as well as additional ECU learning technologies.

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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