Aims and Fit of Module

The module aims to introduce topics in Probability and Statistics and to describe and discuss the potential scope of their applications, and presents the basic concepts and results in Random Processes.
It also trains the students' ability to think logically and independently and to acquire the skills of problem solving.

Learning outcomes

A. describe statistical data;
B. use the Binomial, Geometric, Poisson, Exponential, Raleigh and Normal distributions;
C. demonstrate a good understanding of the basic concepts related to Gaussian random variables,
D. utilize the Law of large numbers and Central Limit Theorem;
E. follow and apply the basic concepts related to Poisson Process, correlation function, stationarity and ergodicity.

Method of teaching and learning

The teaching philosophy of the module follows very much the philosophy of Syntegrative Education. This has meant that the teaching delivery pattern, which follows more intensive block teaching, allows more meaningful contribution from industry partners. This philosophy is carried through also in terms of assessment, with reduction on the use of exams and increase in coursework, especially problem-based assessments that are project focused. The delivery pattern provides space in the semester for students to concentrate on completing the assessments.
This module will be delivered by a combination of formal lectures, problem classes, case studies and revision seminars.