Applications of Computer Science

Module Provider: School of Mathematical and Physical Sciences
Number of credits: 20 [10 ECTS credits]
Level:4
Terms in which taught: Autumn / Spring / Summer module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Module version for: 2016/7

Summary module description:
This module introduces popular applications associated with computers, including artificial intelligence, robotics, computer vision and graphics, and data analytics.

Aims:
The module aims to broaden students? knowledge of computer science with its applications in the key areas to enhance their understanding to the discipline.

Assessable learning outcomes:
Students completing this module should be able to describe typical techniques and apply relevant algorithms to artificial intelligence and robots, to use basic algorithms describing tasks involved in computer vision and computer graphics; and to deal with data workflows with relevant data analytical tools.

Additional outcomes:
Outline content:
The module consists of four application components, as listed below

-Artificial intelligence: here various methods are discussed which are used for ?intelligent? computing machines; it is also shown how some of these methods, such as neural networks and evolutionary computing, have been inspired by natural systems. Applications for artificial intelligence algorithms are also considered.

-Robotics: a cybernetic approach to the subject is taken, showing how the theme of feedback is key to the control of, interaction with and learning by robots. Different types and applications of robots are described, their ?brain?, sensors and actuators are explained, interaction between robots and between humans and robots are considered, and links with artificial life and artificial intelligence are explored.

-Computer vision and graphics: typical cases of successful computer vision applications are introduced. Techniques of computer graphics supporting games and virtual reality are presented.

-Data analytics: concepts, techniques and tools for the extraction of information from data and for the design and execution of data workflows are introduced.
Brief description of teaching and learning methods:
The module comprises 2 lectures per week, associated laboratory practicals, assignments and some revision tutorials. Laboratory practicals are used to reinforce the relevant lectures.

Contact hours:
Lectures- 20
Practicals classes and workshops- 10
Guided independent study- 68
Total hours by term- 98

Summative Assessment Methods:
Written exam- 70%
Sex exercise- 30%

Other information on summative assessment:
Formative assessment methods:

Length of examination:
One 3-hour examination paper in May/June.

Requirements for a pass:
40%

Reassessment arrangements:
Examination only.
One 3-hour examination paper in August/September.