Computer Science and Artificial Intelligence with Year in Industry BSc

Spending four hours a week in lectures and computer classes, you’ll cover the following topics:

• security of the computer
• security of networks
• security and the internet
• software and hardware security
• mobile security
• basic cryptography

This module looks broadly into professional ethics within the scope of the computing discipline. It covers a range of professional, ethical, social and legal issues in order to study the impact that computer systems have in society and the implications of this from the perspective of the computing profession. In particular, the module covers topics such as introduction to ethics, critical thinking, professionalism, privacy, intellectual and intangible property, cyber-behaviour, safety, reliability accountability, all these within the context of computer systems development.

In this module, you will consider the design of collaboration and communication technologies used in a variety of different contexts including workplace, domestic and leisure environments. You will consider the basic principles of such technologies, explore the technologies from a social perspective, consider their impact on human behaviour and critically reflect on their design from a human-centred perspective.

You are given the opportunity to combine your developing CCT knowledge with your programming abilities. You have the whole semester to build a working collaborative project either individually, or you can opt to work in a team, and produce a report on how it supports collaboration according to CCT theory. The primary focus is on building a working application, and so existing strong programming ability is required. 

You’ll examine aspects of language and compiler design by looking at the techniques and tools that are used to construct compilers for high level programming languages. Topics covered include: parsing; types and type systems; run-time organisation; memory management; code generation; and optimisation. You’ll spend around four hours each week in lectures and computer classes. 

You will begin by considering the attempts to characterise the problems that can theoretically be solved by physically possible computational processes, along with the practical implications. You will then consider the area of complexity theory, looking at whether or not problems can be solved under limitations on resources such as time or space. You will examine the classes P and NP, and how to show problems are NP-complete. You will also consider other practically important classes such as: PSPACE, and its relevance to adversarial games, ontologies, and the semantic web; and also complexity classes such as NC relevant to understanding of parallel computation and the limitations of its effectiveness.

You’ll examine the principles of 3D computer graphics, focusing on modelling the 3D world on the computer, projecting onto 2D display and rendering 2D display to give it realism.

Through weekly lectures and laboratory sessions, you’ll explore various methods and requirements in 3D computer graphics, balancing efficiency and realism.

You’ll examine current techniques for the extraction of useful information about a physical situation from individual and sets of images.

You’ll cover a range of methods and applications, with particular emphasis being placed on the identification of objects, recovery of three-dimensional shape and motion, and the recognition of events.

You’ll learn how to implement some of these methods in the industry-standard programming environment MATLAB.

You’ll spend around three hours a week in lectures and laboratory sessions.

This module will enable you to appreciate the range of data analysis problems that can be modelled computationally and a range of techniques that are suitable to analyse and solve those problems.

Topics covered include:

• basic statistics
• types of data
• data visualisation techniques
• data modelling
• data pre-processing methods including data imputation
• forecasting methods
• clustering and classification methods (decision trees, naīve bayes classifiers, k-nearest neighbours)
• data simulation
• model interpretation techniques to aid decision support

Spending around four hours each week in lectures and computer classes, appropriate software (eg. R, Weka) will be used to illustrate the topics you'll cover.

You’ll be given a basic introduction to the analysis and design of intelligent agents, software systems which perceive their environment and act in that environment in pursuit of their goals.

You’ll cover topics including:

• task environments
• reactive, deliberative and hybrid architectures for individual agents
• architectures and coordination mechanisms for multi-agent systems

You will spend around four hours each week in lectures and tutorials for this module.

Students taking part in activities relating to programming experience such as developing apps in their spare time, contributing to open source projects, or building things in hackathons may receive academic credit for showing they have experience and excellent development skills. The emphasis of this module is that you provide evidence of your significant extra-curricular software development experience. Students will only be able to register for this module with the approval of the convenor/school, once the material for assessment has been checked.

Information Visualisation is the process of extracting knowledge from complex data, and presenting it to a user in a manner that this appropriate to their needs. This module provides a foundational understanding of some important issues in information visualisation design. You will learn about the differences between scientific and creative approaches to constructing visualisations, and consider some important challenges such as the representation of ambiguous or time-based data. You will also learn about psychological theories that help explain how humans process information, and consider their relevance to the design of effective visualisations.

If you want to learn how to design and implement your own interactive information visualisation, you should also take the linked module G53IVP (Information Visualisation Project). Together, these two modules form an integrated 20 credit programme of study.

This module is an introduction to the design of human-AI interaction to ensure the AI-driven systems we build are beneficial and useful to people.

The module will cover practical design topics including methods and techniques such as natural language processing and human-robot interaction. The module will also consider societal and theoretical concerns of human-AI interaction, including the ethics of AI, responsible innovation, trust, accountability and explainable AI.

The practical component of the module will involve building AI-driven systems that drive conversational experiences, such as a text-based ‘chatbots’ and speech-controlled services/ ‘skills’, involving automatic speech recognition and natural language processing.

You’ll perform an individual project on a topic in computer science. You’ll produce a 15-25,000 word project report under the guidance of your supervisor, who you will meet with for an hour each week.

The topic can be any area of the subject which is of mutual interest to both the student and supervisor, but should involve a substantial software development component.

In this module you will gain practical experience of how to design and evaluate a distinctive interactive visualisation which presents information gathered from a complex and interesting data source.

You will gain experience in web-based technologies that enable the implementation of multi-layered and interactive information visualisations, supported through lab work that introduces specific features of these technologies.

This module will require some challenging programming work and assumes some basic knowledge of HTML, CSS and Javascript. Introductory tutorials will be provided to those without this prior knowledge.

Providing an introduction to machine learning, pattern recognition, and data mining techniques, this module will enable you to consider both systems which are able to develop their own rules from trial-and-error experience to solve problems as well as systems that find patterns in data without any supervision. 

You’ll cover a range of topics including:

• machine learning foundations
• pattern recognition foundations
• artificial neural networks
• deep learning
• applications of machine learning
• data mining techniques
• evaluating hypotheses

You’ll spend around six hours each week in lectures and computer classes for this module.

Students taking part in approved activities, such as running code clubs in schools, organising school computing activity days, or becoming active STEM ambassadors, may receive academic credit for demonstrating they have actively contributed to the development of younger students. Students will have undertaken an agreed number of hours on the activities, identified personal goals and targets in relation to these activities and maintained a reflective portfolio as a record of evidence of their competence and achievements. Students will only be able to register for this module with the approval of the convenor/school, once the material for assessment has been discussed.

Students will be introduced to concepts and techniques that are widely used in industry to develop high quality software.

Through a two hour lecture each week, you will be introduced to concepts and techniques that are widely used in industry to develop high quality software. These include the following:

• What makes high quality software? Including procedures and approaches to quality management and quality assurance for software projects. Also, a brief history of software metrics
• Software testing. Including unit testing, integration testing, and acceptance testing, with a particular emphasis upon testing strategy and the automation of testing
• Software deployment. Including techniques used to minimise risk, and also continuous integration

These will all be put into the context of recent industry trends. Training will also be provided in common tools and techniques that are used in professional software development including:

• Version control and the use of code repositories
• Release/configuration Build management tools
• Automated testing frameworks

You’ll look at the development of software applications for mobile devices, with a practical focus on the Android operating system. You’ll consider and use the software development environments for currently available platforms and the typical hardware architecture of mobile devices. You’ll spend around three hours per week in lectures and computer classes.