Overview

This unit covers the theory, techniques and applications of optimisation, with a focus on applications in data analytics. The emphasis is on advanced methods for nonlinear continuous optimisation. In addition to its theoretical description of optimisation algorithms, the unit also has a strong practical focus that requires you to solve … For more content click the Read More button below.

Offerings

S2-01-CLAYTON-ON-CAMPUS

Rules

Enrolment Rule

Contacts

Chief Examiner(s)

Professor Andreas Ernst

Unit Coordinator(s)

Professor Andreas Ernst

Notes

IMPORTANT NOTICE:
Scheduled teaching activities and/or workload information are subject to change in response to COVID-19, please check your Unit timetable and Unit Moodle site for more details.

This unit is offered in alternate years commencing Semester 1, 2019.

Learning outcomes

On successful completion of this unit, you should be able to:
1.

Develop specialised mathematical knowledge in nonlinear optimisation algorithms and their efficient computer implementation

2.

Understand the connection between optimisation and the training of data science models.

3.

Determine an appropriate choice of optimisation approach based on problem characteristics.

4.

Apply sophisticated optimisation methods to large problems arising from data analytics

5.

Translate the result of optimisation into the application domain

6.

Apply critical thinking in the field of computational optimisation

Teaching approach

Active learning

Assessment

1 - In-semester assessment
2 - Examination (3 hours and 10 minutes)

Scheduled and non-scheduled teaching activities

Applied sessions
Lectures

Workload requirements

Workload

Availability in areas of study

Master of Mathematics