Computer Science - U6UCMPSC

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.

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.

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

This module is part of the Human Computer Interaction theme in The School of Computer Science. This module aims to teach an understanding of 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. Specifically, this module will cover an understanding of different styles of interaction with technology, an analysis of user needs, design standards, low fidelity prototyping techniques and a comparison of evaluation techniques.

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.

In this module, you will gain a broad overview of the fundamental theories and techniques of cyber-physical systems. You will explore the architecture of cyber physical systems as a network of interacting elements with physical input and output and intelligent mechanisms, and develop an understanding of how to increase their adaptability, autonomy, efficiency, reliability, safety, and usability in different contexts of use. 

Building on the material presented in the Foundations of Software Engineering module, 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. You will spend around two hours per week in lectures and one hour in labs for practical experience for this module.

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’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, and NC, and its relevance to understanding of parallel computation and the limitations of its effectiveness. You will also consider classes BPP for probabilistic computation, and BQP for quantum computing, along with ideas of post-quantum cryptography based on NP-hard problems. You will look at the practical implications of computability and complexity in the context of real-world problems, such as vehicle routing, scheduling, timetabling, machine learning, and quantum computing.  

You will learn the principles of 3D computer graphics, focusing on modelling and viewing objects/scene in a three-dimensional (3D) world on the computer, rendering the objects/scene to give it realism, and projecting objects/scene onto 2D display in analogy to your taking a photo of a 3D world using a camera. Through weekly lectures and laboratory sessions, you will explore various computer graphics techniques and develop your graphics programming skills, including the use of specialist APIs, required for 3D computer graphics. The module demonstrates the benefits of linking theory and practice.

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 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.

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.

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.

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.

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.

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.

The module promotes understanding of the relationships between software and society. We cover key historical and contemporary developments in software to develop a holistic perspective of the role software plays in the wider social world. We also consider how current research and theory can help us understand the impacts of software, including how impacts differ across different demographics. In addition, the module explores how software can benefit social good, for instance through quality and sustainability.

Examples of topics covered include:
• Understanding software as sociotechnical
• Exploring the impacts of social media
• Theories of software impacts: technological determinism vs the social shaping of technology
• Workplace cultures and diversity
• Innovation vs regulation
• Software quality and sustainability
• Forms of bias and their impacts

This course 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: propositional and first-order logics; resolution; SAT; constraint satisfaction; description logics and non-monotonic reasoning. You will have two hours of lectures and three hours of labs each week for this course. You will get hands-on experience of formulating real-world problems and using state-of-the-art reasoning software packages to solve them.