Faculty of Engineering

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This unit entry is for students who completed this unit in 2016 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook. If you have any queries contact the managing faculty for your course or area of study.

Monash University

6 points, SCA Band 2, 0.125 EFTSL

Undergraduate - Unit

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



Organisational Unit

Department of Electrical and Computer Systems Engineering


Prof Andreas Ernst (Clayton), Mr Nader Kamrani (Malaysia)



  • First semester 2016 (Day)


  • First semester 2016 (Day)


This unit will introduce students to matrix decomposition methods including singular value decomposition with applications including data compression, image processing, noise filtering, and finding exact and approximate solutions of linear systems. Numerical methods for working efficiently with large matrices and handling ill-conditioned data will be discussed. Methods for unconstrained and constrained optimisation will be presented, with use of MATLAB. The second half of the unit will focus on stochastic processes in both discrete and continuous time, with applications to time series modelling, and circuit analysis.


On completing this unit, students will have learned advanced mathematical techniques for working efficiently and reliably with both deterministic and stochastic systems, and their use in solving problems frequently arising in engineering applications such as solving linear systems, solving systems of differential equations, handling noise, modelling control systems, time series analysis, and studying stability in dynamical systems. Students will develop a rich set of techniques: Eigen analysis greatly simplifies the calculations for many numerical tasks; singular value decomposition and principal component analysis provide powerful tools for data compression and noise filtering; curve fitting methods for estimation, and optimization tools add to the toolkit of techniques students will learn to enable them to tackle a range of practical engineering problems. Students will also have learnt how to work with discrete and continuous random variables and some important distributions, random vectors and their covariance matrices, calculating best linear predictors, modeling using
random sequences and stochastic processes in continuous time, autocovariance functions, transfer functions, spectral density and linear filters, ARMA models and finding best linear predictors for stationary processes.


Continuous assessment: 30%
Examination: (3 hours): 70%.
Students are required to achieve at least 45% in the total continuous assessment component (assignments, tests, mid-semester exams, laboratory reports) and at least 45% in the final examination component and an overall mark of 50% to achieve a pass grade in the unit. Students failing to achieve this requirement will be given a maximum of 45% in the unit.

Workload requirements

3 hours lectures, 2 hours laboratory and practice classes and 7 hours of private study per week

See also Unit timetable information

Chief examiner(s)