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

  Machine Learning and Data Visualisation

• Unit Code

  MAT3120

• Year

  2018

• 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 principles and practices of machine learning to uncovering patterns and trends in complex data sets and then to visualising 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. Data may be derived from DNA sequencing, meteorological observations, social media, drug discovery, travel industry and much more.

Prerequisite Rule

Students must have passed MAT1114 Introductory Statistics or ECF1151 Quantitative and Statistical Techniques for Business or equivalent unit.

Learning Outcomes

On completion of this unit students should be able to:

1. Demonstrate an understanding of the fundamental principles of machine learning and data visualisation.
2. Critically assess the strengths and weaknesses of a range of machine learning methodologies as used in a range of applications.
3. Select the machine learning algorithm most appropriate for a given real-world application and implement that method using existing computational libraries.
4. Train a range of machine learning algorithms using a variety of big data sets and interpret the output.
5. 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.

Additional Learning Experience Information

Laboratories, lectures, self-directed study.

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.

Due to the professional competency skill development associated with this Unit, student attendance/participation within listed in-class activities and/or online activities including discussion boards is compulsory. Students failing to meet participation standards as outlined in the unit plan may be awarded an I Grade (Fail - incomplete). Students who are unable to meet this requirement for medical or other reasons must seek the approval of the unit coordinator.

^ Mandatory to Pass

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