Computer Science and Artificial Intelligence with Year in Industry BSc

You will gain a basic understanding of the fundamental architecture of computers and computer networks.

You’ll learn how the simple building blocks of digital logic can be put together in different ways to build an entire computer.

You’ll also learn how modern computer systems and networks are constructed of hierarchical layers of functionality which build on and abstract the layers below.

You will spend five hours per week in tutorials, lectures and computer classes.

This module considers both the structure of databases, including how to make them fast, efficient and reliable, and the appropriate user interfaces which will make them easy to interact with for users. You will start by looking at how to design a database, gaining an understanding of the standard features that management systems provide and how you can best utilise them, then develop an interactive application to access your database.

Through the lectures and computing sessions you will learn how to design and implement systems using a standard database management system, web technologies and GUI interfaces through practical programming/system examples.

You will gain a broad overview of the fundamental theories and techniques of artificial intelligence (AI).

You’ll explore how computers can produce intelligent behaviour, and will consider topics such as the history of AI, AI search techniques, neural networks, data mining, philosophical and ethical issues, and knowledge representation and reasoning.

You will spend two hours per week in lectures for this module. 

You will be introduced to the concept of software engineering and will be taken through the software development process: deciding exactly what should be built (requirements and specification), designing how it should be built (software architecture), development strategies (implementation and testing), and maintaining change (software evolution and maintenance).

You’ll cover the basic concepts in mathematics which are of relevance to the computer scientists.

These include:

• logic
• sets
• functions and relations
• graphs
• induction
• basic probability
• statistics and matrices

You'll cover the following basic concepts in mathematics which are of relevance to the development of computer software. Topics which will be covered include linear algebra and calculus. 

The module introduces basic principles of programming and algorithms. It covers fundamental programming constructs, such as types and variables, expressions, control structures, and functions.

You'll learn how to design and analyse simple algorithms and data structures that allow efficient storage and manipulation of data. You'll also become familiar with basic software development methodology.

You will spend around six hours per week in lectures, computer classes and tutorials.

In this module you will learn the basic principles of the object-oriented and functional approaches to programming, using the languages Java and Haskell. You will also see how they can be used in practice to write a range of different kinds of programs.

This module runs alongside 'Computer Fundamentals' and provides an expanded view by considering how real computer systems (such as ARM, x86, Linux and *BSD) and networks work.

You’ll also cover the principles of the lower level implementation of I/O using polling and interrupts, and the use of exceptions; how memory and storage are organized as well addressing the issues arising from multicore systems. 

You’ll spend around five hours per week in tutorials, lectures and computer classes.

This module covers important aspects of algorithms, namely their correctness and efficiency.

You’ll study topics such as:

• proofs in propositional logic and predicate logic
• classical vs. intuitionistic reasoning
• basic operations on types
• verification of list based programs
• introduction to program specification and program correctness

To address the issue of efficiency we cover the use of mathematical descriptions of the computational resources needed to support algorithm design decisions. The emphasis is upon understanding data structures and algorithms so as to be able to design and select them appropriately for solving a given problem.

This module builds on the Fundamentals of Artificial Intelligence module. The emphasis is on building on the AI research strengths in the School.

You will be introduced to key topics such as AI techniques, fuzzy logic and planning, and modern search techniques such as Iterated Local Search, Tabu Search, Simulated Annealing, Genetic Algorithms, and Hyper-heuristics, etc.

You will also explore the implementation of some AI techniques.

To build on first year programming modules and further develop programming ability and experience, including ability to develop and understand a large piece of software, build user interfaces and follow a realistic design and testing procedure.

Topic examples include: design diagrams and modelling; GUI programming; testing software engineering methodologies (including agile development and tools), refactoring; design patterns and SOLID principles; all in the context of understanding anddeveloping maintainable third-party code. You will spend around three hours per week in lectures and two hours per week in computer classes studying for this module.

You'll investigate classes of formal language and the practical uses of this theory, applying this to a series of abstract machines ultimately leading to a discussion on what computation is and what can and cannot be computed.

You'll focus in particular on language recognition, but will study a range of topics including:

• finite state machines
• regular expressions
• context-free grammars
• Turing machines
• Lambda calculus

This module builds on parts of the ACE module addressing data structures and formal reasoning and introduces concepts which are important to understand the analysis of algorithms in terms of their complexity.

 This course covers the fundamental principles that underpin operating systems and concurrency. Topics covered include the architecture of operating systems, process and memory management, storage, I/O, and virtualisation. The principles of concurrency will be introduced from both the perspective of an operating system and user applications. Specific topics on concurrency include: hardware support for concurrency; mutual exclusion and condition synchronisation; monitors; safety and liveness properties of concurrent algorithms, and the use of threads and synchronisation.

Working in groups of around five to six people, you’ll be assigned a supervisor who will provide you with a short written description of a computer application to be designed, programmed, and documented during the course of the module. Each group will meet twice a week, once with your supervisor and once without; you’ll also have four introductory one hour lectures. 

Building upon the introductory Functional Programming module in year one, you’ll focus on a number of more advanced topics such as: 

• programming with effects
• reasoning about programs
• control flow
• advanced libraries
• improving efficiency
• type systems
• functional pearls

You’ll spend around four hours per week in lectures and computer classes.

You will cover the programming material and concepts necessary to obtain an understanding of the C++ programming language. You will spend around four hours per week in lectures and computer classes and will be expected to take additional time to practice and to produce your coursework.

This module covers the following topics:

• overview of parallel and distributed computing
• applications of distributed systems
• fundamental concepts of distributed systems (processes and message passing, naming and discovery, fault tolerance and partial failure, consistency and cacheing, security)
• reliable network communication
• distributed system design approaches (direct vs indirect communication, client-server vs peer-to-peer, stateful vs stateless interfaces)
• introduction to distributed data management
• introduction to distributed algorithms

An overview of the field of human computer interaction which aims to understand people's interactions with technology and how to apply this knowledge in the design of usable interactive computer systems.

The module will introduce the concept of usability and will examine different design approaches and evaluation methods.

This module introduces the field of digital image processing, a fundamental component of digital photography, television, computer graphics and computer vision.

You’ll cover topics including:

• image processing and its applications
• fundamentals of digital images
• digital image processing theory and practice
• applications of image processing

You’ll spend around three hours in lectures and computer classes each week.

You will cover two main aspects of the software engineering process in depth: requirements and design. This will cover modern approaches to large scale requirements and engineering and specification and approaches to systems and architectural design. 

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.

You'll study the theory used in the design and analysis of advanced algorithms and data structures. Topics covered include string algorithms (such as for string matching, longest common subsequence), graph algorithms (such as for minimum cuts and maximum flows, and Google's pagerank algorithm), advanced data structures (such as Fibonacci heaps and Bloom filters), and randomised search heuristics (evolutionary algorithms). You'll learn all the necessary probability theory will be introduced, including random variables and concentration inequalities.

The theory is practiced in weekly labs where we learn how to implement the algorithms and data structures as functional and imperative programs (using the languages Haskell and C), and apply these to solve large instances of real-world problems. 

This module will provide you with an advanced knowledge of computer communications networks, using examples from all-IP core telecommunications networks to illustrate aspects of transmission coding, error control, media access, internet protocols, routing, presentation coding, services and security.

The module will describe Software Defined Networks (SDNs) and provide examples of using them to enable very large scale complex network control. It will also provide advanced knowledge of various routing and query protocols in:

• Ad Hoc Networks
• Mobile Ad Hoc Networks (MANETs)
• Vehicular Ad Hoc Networks (VANETs)
• Disconnection/Disruption/Delay Tolerant Networks (DTNs)
• impact of new networking developments, such as security risks, ethics, interception and data protection will be reflected and discussed systematically

This module introduces you to the computer science of robotics, giving you an understanding of the hardware and software principles appropriate for control and localisation of autonomous mobile robots. A significant part of the module is laboratory-based, utilising physical robotic hardware to reinforce the theoretical principles covered.

Spending around three to four hours each week in lectures and practicals, you’ll cover a range of topics including:

• basic behavioural control architectures
• programming of multiple behaviours
• capabilities and limitations of sensors and actuators
• filtering techniques for robot localisation

'Big Data' involves data whose volume, diversity and complexity requires new technologies, algorithms and analyses to extract valuable knowledge, which go beyond the normal processing capabilities of a single computer. The field of Big Data has many different faces such as databases, security and privacy, visualisation, computational infrastructure or data analytics/mining.

'Big Data' involves data whose volume, diversity and complexity requires new technologies, algorithms and analyses to extract valuable knowledge, which go beyond the normal processing capabilities of a single computer. The field of Big Data has many different faces such as databases, security and privacy, visualisation, computational infrastructure or data analytics/mining.

This module will provide the following concepts:

1. Introduction to Big data: introducing the main principles behind distributed/parallel systems with data intensive applications, identifying key challenges: capture, store, search, analyse and visualise the data.
2. SQL Databases vs. NoSQL Databases: understand the growing amounts of data; the relational database management systems (RDBMS); overview of Structured Query Languages (e.g. SQL); introduction to NoSQL databases; understanding the difference between a relational DBMS and a NoSQL database; Identifying the need to employ a NoSQL DB.
3. Big Data frameworks and how to deal with big data: this includes the MapReduce programming model, as well as an overview of recent technologies (Hadoop ecosystem, and Apache Spark). Then, you will learn how to interact with the latest APIs of Apache Spark (RDDs, DataFrames and Datasets) to create distributed programs capable of dealing with big datasets (using Python and/or Scala)
4. Finally, we will dive into the data mining and machine learning part of the course, including data preprocessing approaches (to obtain quality data), distributed machine learning algorithms and data stream algorithms. To do so, you will use the Machine learning library of Apache Spark (MLlib) to understand how some machine learning algorithms (e.g. Decision Trees, Random Forests, k-means) can be deployed at a scale.

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’ll begin by considering the attempts to characterise the problems that can theoretically be solved by physically-possible computational processes.

You’ll 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. A key topic is an examination of the classes P and NP and the definition of the term NP-complete.

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 detection and identification of objects, image segmentation, pose estimation, recovery of three-dimensional shape and analysis of motion.

These problems will be approached with both traditional and modern Computer Vision approaches including Deep Learning.

You will spend five hours per week in lectures, tutorials, and computer classes for this module.

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 aims to provide a thorough understanding of fuzzy sets and systems from a theoretical and practical perspective.

Topics commonly include:

• type-1 fuzzy sets
• type-1 fuzzy logic systems
• type-1 fuzzy set based applications
• type-2 fuzzy sets
• type-2 fuzzy logic systems
• type-2 fuzzy set based applications.

You will also be exposed to some of the cutting-edge research topics in uncertain data and decision making, e.g., based on type-2 fuzzy logic as well as other fuzzy logic representations. You will develop practical systems and software in a suitable programming language.

This module covers the history, development and state-of-the-art in computer games and technological entertainment.

You will gain an appreciation of the range of gaming applications available and be able to chart their emergence as a prevalent form of entertainment. You will study the fundamental principles of theoretical game design and how these can be applied to a variety of modern computer games.

In addition, you will study the development of games as complex software systems. Specific software design issues to be considered will include the software architecture of games, and the technical issues associated with networked and multiplayer games.

Finally, you will use appropriate software environments to individually develop a number of games to explore relevant theoretical design and practical implementation concepts.

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.

This module examines how knowledge can be represented symbolically and how it can be manipulated in an automated way by reasoning programs. Some of the topics you’ll cover include: first order logic; resolution; description logic; default reasoning; rule-based systems; belief networks and fuzzy logic. You’ll have two hours of lectures each week.

This module provides an entry point to computational optimisation techniques, in particular for modelling and solving linear and discrete optimisation problems like diet optimisation, network flows, task assignment, scheduling, bin-packing, travelling salesmen, facility location, vehicle routing and related problems.

In this module, you will learn to interpret and develop algebraic models for a variety of real-world linear and discrete optimisation problems to then use powerful optimization software (linear, integer and mixed-integer solvers) to produce a solution.

The module covers topics such as:

• linear programming
• integer programming
• combinatorial optimisation
• modelling and optimisation software
• multi-objective optimisation 

Optimisation technology is ubiquitous in today's world, for applications in logistics, finance, manufacturing, workforce planning, product selection, healthcare, and any other area where the limited resources must be used efficiently. Optimisation enables prescriptive analytics in order to support and automate decision-making.

Providing you with 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. In the latter case, data mining techniques will make the generation of new knowledge possible, including very big data sets. This is now fashionably termed 'big data' science.

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 and evaluating hypotheses

This module looks at the practice of malware analysis, looking at how to analyse malicious software to understand how it works, how to identify it, and how to defeat or eliminate it.  

You will look at how to set up a safe environment in which to analyse malware, as well as exploring both static and dynamic malware analysis. Although malware takes many forms, the focus of this module will primarily be on executable binaries. This will cover object file formats and the use of tools such as debuggers, virtual machines, and disassemblers to explore them. Obfuscation and packing schemes will be discussed, along with various issues related to Windows internals.

The module is practical with encouragement to safely practice the skills you're taught.

This module focuses on the possibilities and challenges of interaction beyond the desktop. Exploring the 'mixed reality continuum' - a spectrum of emerging computing applications that runs from virtual reality (in which a user is immersed into a computer-generated virtual world) at one extreme, to ubiquitous computing (in which digital materials appear embedded into the everyday physical world - often referred to as the 'Internet of Things') at the other. In the middle of this continuum lie augmented reality and locative media in which the digital appears to be overlaid upon the physical world in different ways.

You will gain knowledge and hands-on experience of design and development with key technologies along this continuum, including working with both ubiquitous computing based sensor systems and locative media. You will learn about the Human-Computer Interaction challenges that need to be considered when creating mixed reality applications along with strategies for addressing them, so as to create compelling and reliable user experiences.

This project involves a self-guided study of a selected advanced algorithm or data structure. The outcome of the project is an analysis and implementation of the algorithm or data structure, as well as an empirical evaluation, preferably on a real-world data set of significant size.

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.

This module offers insight into the applications of selected methods of decision support.

The foundations for applying these methods are derived from:

• Operations Research Simulation
• Social Simulation
• Data Science
• Automated Scheduling
• Decision Analysis

Throughout the module, you will become more competent in choosing and implementing the appropriate method for the particular problem at hand. You will spend five hours per week in lectures, workshops, and computer classes for this module.

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