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.
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 to:
- demonstrate a sophisticated understanding of concepts in probability, statistical inference and signal processing
- critically apply data analysis techniques to real engineering problems
- make sound conclusions from experimental data
- demonstrate proficiency in use of a computer software for data analysis
- demonstrate proficiency in presenting with data.
Continuous assessment: 50%
Examination (3 hours): 50%
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.
3 hours lectures, 2 hours of labs and 7 hours of private study per week.
See also Unit timetable information