0 points, SCA Band 2, 0.000 EFTSL
Postgraduate - Unit
Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.
Dr Harish Vangala
Dr Chang Fun Liang (Malaysia)
This unit is available only to Engineering PhD students.
The unit consists of a review of probabilistic foundations for data analysis including probability, random variables, expectation, distribution functions, important probability distributions, central limit theorem, random vectors, conditional distributions and random processes.
Students will develop the foundations of statistical inference including estimation, confidence intervals, maximum likelihood, hypothesis testing, least-squares and regression analysis.
A selection of more advanced topics in probability, random modelling and statistical inference will also be presented.
The material will be taught in the context of real engineering problems taken from multiple engineering disciplines. A widely used numerical computing environment will be used extensively throughout the unit.
On successful completion of this unit students should be able:
- assess problems from an engineering perspective and deliberate on the relevant contextual factors. Combine and apply sophisticated data analysis methods and decision-making skills to analyse industrial scenarios and make recommendations that support business growth and development.
- justify the use of appropriate computer modelling techniques and experimental methods, whilst ensuring model or test applicability, accuracy and limitations of the methods.
- collaboratively evaluate an industry scenario to solve a problem or develop an innovation.
- demonstrate the effective communication of the outcomes in a written and verbal format and assess the work of others.
Continuous assessment: 60%
Examination: (2 hours) 40%
Students are required to achieve at least 45% in the total continuous assessment component and at least 45% in the final examination component and an overall mark of 50% to achieve a pass grade in the unit.
2 x 1-hour lectures, 2 hours of labs and 8 hours of private study per week including online work.
See also Unit timetable information