CS1AC16-Applications of Computer Science

Module Provider: Computer Science
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:
Current from: 2020/1

Module Convenor: Prof Richard Mitchell

Email: r.j.mitchell@reading.ac.uk

Type of module:

Summary module description:

This module introduces popular applications associated with computers, including artificial intelligence, robotics, virtual reality, computer visionÌýand data analytics.

Aims:

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

This module also encourages students to develop a set of professional skills such as problem solving. Some aspects of social and Legal aspects of artificial intelligence, robotics and vision are considered.

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 themes, as listed below:

• Artificial intelligence: here various methods are discussed which are used for ‘intelligent’ computing machines. These include classical AI methods such as Expert Systems and Problem Solving, as well as neural networks and evolutionary computing methods which, have been inspired by natural systems. Applications for artificial intelligence algorithms are also considered;

• Robotics, Artificial Life and Virtual Reality: a cybernetic approach to these subjects is taken, showing how the theme of feedback is key to the control of robots, interaction with robots, computers and humans, and learning by robots and other forms of artificial life. Different types and applications of robots are described, their history given and their brain, sensors and actuators are discussed. Interaction is explored in terms of robots interacting with humans and robots, and humans i n Virtual Reality systems, for which computer graphics and haptics are also discussed. Concepts in artificial life are explored, including mobile robots, Game of Life, Daisyworld as well as Fractals and, Lindemayer systems which can be used in virtual worlds;

• Computer vision:Ìý this is the science behind development of capability to emulate (or possibly exceed) human's ability to visually sense the world, and is concerned with the automatic extraction, analysis and understanding of useful information from a single image or multiple images.Ìý This block of lectures will specifically focus on of some of most important methodologies and applications of computer vision and include topics such as biometrics, detection and tracking, deep learning, and behavioural recognition.Ìý The lectures cover both the underpinning theory behind the different topics presented as well as a deeper understanding of how the methods are applied in the real world;

< br />

• Data analytics: Students are introduced to the concept of extracting useful information from data, covering types of data, data sources, pre-processing and manipulation techniques, feature selection and transformation, and data visualisation. These concepts are applied with hands-on activities using KNIME, an open source data workflow tool for advanced analytics.

Brief description of teaching and learning methods:

The module comprises weekly lectures, an online course, associated laboratory practicals, assignments and some revision tutorials. Laboratory practicals are used to reinforce the relevant lectures. Revision lectures occur in the summer term.

Contact hours:

Ìý	Autumn	Spring	Summer
Lectures	20	15	4
Practicals classes and workshops	6	6
Guided independent study:	72	77
Ìý	Ìý	Ìý	Ìý
Total hours by term	98	98	4
Ìý	Ìý	Ìý	Ìý
Total hours for module	200

Summative Assessment Methods:

Method	Percentage
Written exam	70
Set exercise	30

Summative assessment- Examinations:

One 3-hour examination paper in May/June.

Summative assessment- Coursework and in-class tests:

For each of the four application themes of the module, there are timetabled sessions in the PC lab where students investigate aspects of AI, Computer Vision, Robotics & VR, and Data Analytics and answer questions posed on Blackboard. The assignments for each of the four themes are worth 7.5%.

Formative assessment methods:

Penalties for late submission:

The Module Convenor will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day[1] (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at:
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:

A mark of 40% overall.

Reassessment arrangements:

One 3-hour examination paper in August/September.Ìý Note that the resit module mark will be the higher of (a) the mark from this resit exam and (b) an average of this resit exam mark and previous coursework marks, weighted as per the first attempt (70% exam, 30% coursework).

Additional Costs (specified where applicable):
1) Required text books:
2) Specialist equipment or materials:
3) Specialist clothing, footwear or headgear:
4) Printing and binding:
5) Computers and devices with a particular specification:
6) Travel, accommodation and subsistence:

Last updated: 16 April 2020