units

ENG6001

Faculty of Engineering

18 September 2017 22 November 2024

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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

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.

Faculty

Engineering

Coordinator(s)

Professor Jean Armstrong

Offered

Clayton

  • First semester 2016 (Day)

Malaysia

  • First semester 2016 (Day)

Notes

This unit is available only to Engineering PhD students.

Synopsis

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.

The unit will then examine the foundations of signal analysis including continuous and discrete-time signals, sampling, quantization, filtering, Fourier analysis, random signals and power spectral density.

A selection of more advanced topics in probability, random modelling, statistical inference and signal processing will also be presented.

The material will be taught in the context of real engineering problems taken from multiple engineering disciplines. The numerical computing environment MATLAB will be used extensively throughout the unit.

Outcomes

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 MATLAB for data analysis
  • demonstrate proficiency in presenting with data

Assessment

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.

Workload requirements

3 hours lectures, 2 hours of labs and 7 hours of private study per week.

See also Unit timetable information

Chief examiner(s)

Prof Tom Drummond

Prerequisites

None

Co-requisites

None

Prohibitions

None