MTH4331 - Optimisation for data analytics - 2019

6 points, SCA Band 2, 0.125 EFTSL

Undergraduate, Postgraduate - Unit

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.



Organisational Unit

School of Mathematical Sciences

Chief examiner(s)

Professor Andreas Ernst


Professor Andreas Ernst

Unit guides



  • Second semester 2019 (On-campus)


Enrolment in the Master of Mathematics and MTH3330




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 with students required to solve problems computationally through programming. Topics covered include a selection from quasi-Newton methods, augmented Lagrangian methods, and stochastic gradient descent methods, with applications to machine learning and neural networks. Furthermore, the unit will cover constrained optimisation methods that may include quadratic programming, interior point methods, as well as stochastic meta-heuristics for nonlinear optimisation. Applications of these methods may include support vector machines and other classification methods.


On completion of this unit students will 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


NOTE: From 1 July 2019, the duration of all exams is changing to combine reading and writing time. The new exam duration for this unit is 3 hours and 10 minutes.

Examination (3 hours): 60% (Hurdle)

Continuous assessment: 40%

Hurdle requirement: To pass this unit a student must achieve at least 50% overall and at least 40% for the end-of-semester exam.

Workload requirements

  • 3 hours of lectures and 1 hour tutorial per week
  • 8 hours of independent study per week

See also Unit timetable information

This unit applies to the following area(s) of study

Master of Mathematics