Autonomous Robotic Systems
20 credits
This module introduces the main concepts of autonomous mobile robotics, providing an understanding of the hardware and software principles appropriate for control, spatial localisation and navigation. The module consists of theoretical concepts around robotic sensing and control in the lectures, together with a strong practical element for robot programming in the laboratory sessions
Big Data Learning and Technologies
20 credits
This module will cover four main concepts.
It will start with an introduction to big data. You’ll find out about the main principles behind distributed/parallel systems with data intensive applications, identifying key challenges such as capture, store, search, analyse and visualise the data.
We’ll also look at SQL Databases verses NoSQL Databases. You will learn:
- the growing amounts of data
- the relational database management systems (RDBMS)
- an overview of Structured Query Languages (SQL)
- an introduction to NoSQL databases
- the difference between a relational DBMS and a NoSQL database
- how to identify the need to employ a NoSQL database
Another concept is 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).
Finally, we will cover the data mining and machine learning part of the course. This will include data preprocessing approaches, distributed machine learning algorithms and data stream algorithms. To do so, you will use the machine learning library of Apache Spark to understand how some machine learning algorithms can be deployed at a scale.
Computer Vision
20 credits
You will examine current techniques for the extraction of useful information about a physical situation from individual and sets of images. You will learn 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.
Data Science with Machine Learning
20 credits
This module explores 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
Designing Intelligent Agents
20 credits
In this module, you will 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 will cover topics including task environments, reactive, deliberative and hybrid architectures for individual agents, and architectures and coordination mechanisms for multi-agent systems.
Handling Uncertainty with Fuzzy Sets and Fuzzy Systems
20 credits
This module focuses on handling uncertainty such as vagueness using fuzzy sets and similar approaches. It provides a thorough understanding of key topics such as:
- the nature of uncertainty captured by fuzzy sets and associated links to human reasoning
- inference using fuzzy sets
- similarity of fuzzy sets
- design and modelling of information via fuzzy sets
- type-1 fuzzy sets
- type-2 fuzzy sets
- fuzzy logic systems
- fuzzy set based applications
Human-AI Interaction
20 credits
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.
Symbolic Artificial Intelligence
20 credits
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
Linear and Discrete Optimisation
20 credits
The module provides an entry point to computational optimization techniques, particularly for modelling and solving linear and discrete optimization problems like diet optimization, network flows, task assignment, scheduling, bin-packing, travelling salesmen, facility location, vehicle routing and related problems.
Optimization sits at the interface of computer science and mathematics. Optimization is considered one of the key techniques within the broad spectrum of artificial intelligence methods. Optimization focuses on making decisions instead of predicting or identifying patterns. Optimization is also one of the most important areas within operations research (OR), which is a discipline that uses modelling techniques, analytics and computational methods to solve complex problems in industry and business.
In this module, you will learn to interpret and develop algebraic models for a variety of real-world linear and discrete optimization 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 optimization, modelling and optimization software, multi-objective optimization, simplex method, and branch and bound method among others. Optimization 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. Optimization enables prescriptive analytics, in order to support and automate decision-making"
Machine Learning
20 credits
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.
Simulation and Optimisation for Decision Support
20 credits
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 engage in a mixture of lectures, workshops, and computer classes.
Advanced Algorithms and Data Structures
10 credits
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.
Advanced Computer Networks
20 credits
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
Autonomous Robotic Systems
20 credits
This module introduces the main concepts of autonomous mobile robotics, providing an understanding of the hardware and software principles appropriate for control, spatial localisation and navigation. The module consists of theoretical concepts around robotic sensing and control in the lectures, together with a strong practical element for robot programming in the laboratory sessions
Computer Graphics
20 credits
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.
Fundamentals of Information Visualisation
10 credits
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.
Information Visualisation Project
10 credits
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.
Symbolic Artificial Intelligence
20 credits
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 will cover include:
- first order logic
- resolution
- description logic
- default reasoning
- rule-based systems
- belief networks
Linear and Discrete Optimisation
20 credits
The module provides an entry point to computational optimization techniques, particularly for modelling and solving linear and discrete optimization problems like diet optimization, network flows, task assignment, scheduling, bin-packing, travelling salesmen, facility location, vehicle routing and related problems.
Optimization sits at the interface of computer science and mathematics. Optimization is considered one of the key techniques within the broad spectrum of artificial intelligence methods. Optimization focuses on making decisions instead of predicting or identifying patterns. Optimization is also one of the most important areas within operations research (OR), which is a discipline that uses modelling techniques, analytics and computational methods to solve complex problems in industry and business.
In this module, you will learn to interpret and develop algebraic models for a variety of real-world linear and discrete optimization 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 optimization, modelling and optimization software, multi-objective optimization, simplex method, and branch and bound method among others. Optimization 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. Optimization enables prescriptive analytics, in order to support and automate decision-making"
Machine Learning
20 credits
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
Malware Analysis
10 credits
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.
Mixed Reality
20 credits
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.
Programs, Proofs and Types
20 credits
This module focuses on some of the fundamental mathematical concepts that underlie modern programming and programming languages emphasizing the role of types. We will use a dependently typed programming language/interactive proof system (eg Agda) to implement some concepts on a computer.
Example topics include
- basic lambda calculus
- operational semantics
- domain theory
- types, propositions as types and formal verification.
You will engage in a mix of lectures and working in the lab with an interactive proof system.
Project in Advanced Algorithms and Data Structures
10 credits
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.
Simulation and Optimisation for Decision Support
20 credits
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 engage in a mixture of lectures, workshops, and computer classes.
Human-AI Interaction
20 credits
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.
Designing Intelligent Agents
20 credits
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.
As part of the assessment of this module you will produce a research paper-style report, and deliver a conference-style presentation.
Games
20 credits
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.
Data Science with Machine Learning
20 credits
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.
You will engage in a mixture of lectures and computer classes where appropriate software (eg R, Weka) will be used.
Handling Uncertainty with Fuzzy Sets and Fuzzy Systems
20 credits
This module focuses on handling uncertainty such as vagueness using fuzzy sets and similar approaches. It provides a thorough understanding of key topics such as:
- the nature of uncertainty captured by fuzzy sets and associated links to human reasoning
- inference using fuzzy sets
- similarity of fuzzy sets
- design and modelling of information via fuzzy sets
- type-1 fuzzy sets
- type-2 fuzzy sets
- fuzzy logic systems
- fuzzy set based applications
Big Data Learning and Technologies
20 credits
This module will cover four main concepts.
It will start with an introduction to big data. You’ll find out about the main principles behind distributed/parallel systems with data intensive applications, identifying key challenges such as capture, store, search, analyse and visualise the data.
We’ll also look at SQL Databases verses NoSQL Databases. You will learn:
- the growing amounts of data
- the relational database management systems (RDBMS)
- an overview of Structured Query Languages (SQL)
- an introduction to NoSQL databases
- the difference between a relational DBMS and a NoSQL database
- how to identify the need to employ a NoSQL database
Another concept is 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).
Finally, we will cover the data mining and machine learning part of the course. This will include data preprocessing approaches, distributed machine learning algorithms and data stream algorithms. To do so, you will use the machine learning library of Apache Spark to understand how some machine learning algorithms can be deployed at a scale.
Computer Vision
20 credits
You will examine current techniques for the extraction of useful information about a physical situation from individual and sets of images. You will learn 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.
Cyber Security
10 credits
Cyber security is an essential consideration for the protection of IT-based devices, systems, networks and data, providing safeguard and reassurance to the organisations and individuals that now rely (and increasingly depend) upon them. We provide coverage of both technical and human perspectives, considering the fundamental threats and safeguards that concern both personal and workplace contexts. You will emerge with the knowledge and skills necessary to enable informed cyber security decisions spanning the technical, human and organisational dimensions of the topic.
You will gain knowledge and practical experience across a range of key cyber security topics, including foundational concepts and principles, authentication and access control, operating system security, cryptographic mechanisms and applications, security management, risk assessment, cyber-attacks and threat intelligence, network and Internet security, intrusion detection and incident response, and human aspects. You will learn about the challenges that need to be considered when designing and implementing secure systems, along with associated approaches to ensure that security is addressed in an effective and holistic manner.