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
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