Computer Science with Artificial Intelligence - U7UCMPAI

This module takes a practical approach to give students a basic understanding of the fundamental architecture of computers and software.

It will introduce low-level machine code instructions and show how these can be combined to form programs. We then look at higher-level programming structures, like conditional statements, loops, arrays and functions, to show how they’re implemented at machine code level. 

This module shows how modern computer systems are made of hierarchical layers of functionality which build on and abstract the layers below.

You’ll begin by learning how the simple building blocks of digital logic can be put together in different ways to build an entire computer. Then we’ll go on to consider how the design can improve performance, how multicore/ multiprocessor systems are programmed and the how the software in an operating systems manages computing resources. 

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 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. 

You’ll learn how  a computer communicates with other computers at a fundamental level. This will help you to build an understanding of the fundamental architecture of computer networks.

We’ll examine how the layers of modern computer systems are implemented in practice, by comparing and contrasting different approaches to solve common problems.

You’ll also discover how modern IP networks can be programmed using the sockets API.

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.

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 covers important aspects of algorithms and data structures. You will study the general principles along with their efficiencies. You will learn the mathematical terms of the computational resources used to support algorithm design decisions.  

You'll study topics such as:

• sorting algorithms

• heaps

• binary search trees

• hashmaps

• graph algorithms

There is emphasis on understanding data structures and algorithms. This will enable you to design and use them for problem-solving. You will develop the mathematical and formal reasoning skills required to understand software systems, in particular their efficiency.  

The module will give you a good working knowledge of some common algorithms and data structures. You will gain an understanding of the issues involved in designing a program for a specific task.

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.

This module gives an overview of core topics in computer security, including the security of individual computers, security of computer networks, Iinternet security, a consideration of security both from a software and hardware point of view, security of mobile devices, basic cryptography, and the wider context for computer security.

This module is an introduction to formal reasoning with applications in program verification and Mathematics. Students will use an interactive proof system like Lean or Coq to learn how to make precise statements and how to verify them using formal proofs. Topics covered include propositional logic, classical principles, predicate logic, the theories of Booleans, natural numbers, lists and trees, and verification of simple algorithms.

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 ideas from earlier in the degree 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.

This module builds on first year programming modules and further develops programming ability and experience, including the 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 and developing maintainable third-party code. 

Working in groups of around six to seven 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. Around half the projects you will bid for have industry partners you will collaborate with.

Each group will meet twice a week, once with your supervisor and once without; you’ll also have four introductory one hour lectures. 

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. You will cover a range of topics including basic behavioural control architectures, multi-source data aggregation, programming of multiple behaviours, capabilities and limitations of sensors and actuators, and filtering techniques.

'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.

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.

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.

Students undertake a programming project for an external client in self-formed groups of two to four students under the supervision of an academic member of staff. The client, which can be a company, charity, research group etc., but not the supervisor, provides a problem that requires a sufficiently challenging piece of software to be developed. The client and project idea could be provided by the students or the supervisor. However, projects must have aspects that are relevant to each student's programme of study; eg, there needs to be an artificial intelligence (AI) aspect if any AI students are involved.

The main assessed outputs are the developed software, including any end-user documentation, along with a 5,000-word document that outlines the development, design and implementation of the software, highlighting the most interesting aspects. The software must be developed in a professional and systematic manner appropriate for the problem domain. The assessment is informed by a statement from the external client on how well the developed software addresses the problem. Additionally, each student submits an individual 5,000-word report explaining his or her own contributions and giving a critical appraisal of how the project went, including group dynamics and the contributions of others.

In this module, you’ll learn how to handle uncertainty, particularly focusing on the vagueness associated with using fuzzy sets and similar approaches. You will gain a thorough understanding of a range of key topics, including:

• the nature of uncertainty captured by fuzzy sets and associated links to human reasoning
• inference using fuzzy sets
• the 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.

You’ll also be exposed to some of the cutting-edge research topics in uncertain data and decision-making, e.g., capturing uncertainty in real-world scenarios, modelling it, and aggregating (uncertain) information from multiple sources.

You will undertake a programming project relevant for AI for an External Client under the supervision of an academic member of staff.

The client, which can be a company, charity, research group etc., provides a problem that requires a sufficiently challenging piece of software to be developed. The client and project idea could be provided by the students or the supervisor. Each project must ultimately be agreed with the concerned Supervisor.

The main assessed outputs are the developed software, including any end-user documentation, along with a 15,000-word document that outlines the development, design and implementation of the software, highlighting the most interesting aspects.

The software must be developed in a professional and systematic manner appropriate for the problem domain.

The assessment is informed by a statement from the External Client on how well the developed software addresses the problem.

Students undertake a research project in computer science supervised by an academic member of staff. The topic should fall within the supervisor's research interests and must further be relevant to the student's programme of study; in particular, projects undertaken by artificial intelligence (AI) students must have a strong AI focus. The project may be proposed by either the supervisor or the student, and may be theoretical, empirical, or even of survey type depending on what is appropriate and feasible for the area and topic. Projects, however, must ultimately be agreed with the supervisor concerned.

The results from the project are to be distilled into a conference-format research paper, authored by the student and constituting the main assessed output. There may, however, be further deliverables as dictated by the nature of the project. Any such deliverables are to be submitted (electronically) as supplementary material. A revised version of the paper, possibly co-authored with the supervisor, may subsequently be submitted for publication to an external venue, such as a conference or journal, if the work is judged to be of sufficiently high standard. 

The aim of the module is to accredit students who have experienced industry work involving independent self-management, proactive work and the ethical application of their knowledge and skills in a real-world context. You will collect evidence of your competence, which you will use in your assessed portfolio, and must reflect on your developed knowledge and understanding of applied software engineering practices in relation to other software engineering principles taught on the course.

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.

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.