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School of Electronic Engineering and Computer Science

MSc Media and Arts Technology Modules

The programme consists of four 15-credit compulsory modules, four 15-credit elective modules, and a 60-credit research project core module (180 credits in total).

Elective modules can be chosen in the three following themes: 1) Sound, Image, and Games, 2) Interaction, Intelligence, and Sensing, and 3) Creative Industry.

Semester A

Semester A consists of two 15-credit compulsory modules and two 15-credit elective modules (four modules in total).

Semester B

Semester B includes two 15-credit compulsory modules and two 15-credit elective modules (four modules in total).

Semesters B-C

A 60-credit MSc research project core module is conducted in Semesters B and C.

Please note that the enrolment in elective modules is subject to satisfaction of module prerequisites, space and timetabling. The module information below is subject to change and may not be available in any one year.

This course relies on perseverance and the willingness to learn. The knowledge and skills that I gained throughout this programme, I now use in my everyday life. From developing my knowledge around project management to creative coding and new media technology, the Media and Arts Technology Masters has benefited my career as a professional musician. It has greatly improved my creativity and inspirations as I now incorporate computer-generated visualisations into my art, with the aim of creating novel audiovisual experiences for my audience. Great work is also recognised, and I'm thankful for the opportunity to co-author an international conference paper along with other brilliant individuals.
— Harold Opara (artist name Houston X), Media and Arts Technology Masters, 2019-20

Please expand the sections below to read more about the modules available.

Compulsory Modules

Sem A - Interactive Digital Multimedia Techniques

Credits: 15.0
Contact: Dr Charalampos Saitis

Description: This is a Master's level course in developing real-time interactive digital media systems. The course will focus on graphics and sound programming, with a secondary emphasis on basic electronic hardware design for sensors and human-computer interfaces. The course will employ widely-used development environments including Arduino, Processing Max/MSP and Jitter, Processing. Course material will be delivered through a combination of lectures, interactive lab sessions, and individual/group exercises (both in and out of class). Generally speaking, each class period will consist of a combination of lecture and interactive lab session.

Assessment:

  • Item 1: 10.00% Assessed Coursework
  • Item 2: 10.00% Assessed Coursework
  • Item 3: 20.00% Assessed Coursework
  • Item 4: 50.00% Assessed Coursework
  • Item 5: 10.00% Assessed Coursework

Level: 7

Sem A - Design for Human Interaction

Credits: 15.0
Contact: Prof Pat Healey

Description: Developments in information technology have radically altered the nature of human communication. Spatial and temporal constraints on communication have been weakened or removed and new structures and forms of communication have developed. For some technologies, such as video conferencing, text messaging and online communities, the importance of understanding their effect on human communication is clear. However, even the success of 'individualistic' technologies, such as spreadsheets, can be shown to depend partly on their impact on patterns of interaction between people. Conversely, some technologies, such as videophones, that are specifically designed to enhance communication can sometimes make it worse. Currently, there is no accepted explanation of how technologies alter, and are altered by, the patterns and processes of human communication. Such an explanation is necessary for effective design of new technologies. This research led module explores these issues by introducing psychological theories of the nature of human communication and socio-historical perspectives on the development and impact of communication technologies. These models are applied to the analysis of new communications technologies and the effects of those technologies on communication patterns between individuals, groups and societies. A variety of different technologies are introduced ranging from systems for the support of tightly-coupled synchronous interactions through to large-scale shared workspaces for the support of extended collaborations. Detailed studies of the effects of different technologies on task performance, communication processes and user satisfaction are reviewed. Particular attention is paid to the notion of communicative success and to the development of metrics that can be used in assessing it. Frameworks for analysing the communicative properties of different media will be introduced as well as approaches to the analysis of communication in groups and organisations.

Assessment:

  • Item 1: 40.00% Assessed Coursework
  • Item 2: 20.00% Assessed Coursework
  • Item 3: 20.00% Assessed Coursework
  • Item 4: 20.00% Assessed Coursework

Level: 7

Sem B - Interactive System Design

Not available 

Sem B - Data Analytics

Credits: 15.0
Contact: Dr Anthony Constantinou

Description: This module focuses on the range of approaches, methodologies, techniques and tools for data analysis, and the use of data analysis findings to inform decision-making in an industrial / business context. It exposes students to a range of industry-standard statistical and data analysis techniques and tools, and fosters awareness of the challenges associated with working with large datasets. The module also covers topics related to the legal, social, ethical and professional issues associated with data storage and analysis. Students will undertake practical work including empirical data analysis and summarisation / presentation of the results to a range of relevant stakeholders.

Assessment:

  • Item 1: 30.00% Assessed Coursework
  • Item 2: 70.00% Assessed Coursework

Level: 7

Elective Modules

Sound, Image, and Games:

Sem A - Sound Recording and Production Techniques

Credits: 15.0
Contact: Dr Mathieu Barthet

Description: The module develops the students' skills and understanding of contemporary audio production techniques. It will give the students a good grounding in the theoretical aspects of audio production, from the functionality of audio interfaces to the signal processing within audio effects, as well as providing practical experience in the use of all audio equipment to which the theory applies. The students will learn the implications of audio digitisation, through which they will gain an understanding of the various means by which digital media is disseminated in the modern age.

Assessment:

  • Item 1: 25.00% Assessed Coursework
  • Item 2: 25.00% Assessed Coursework
  • Item 3: 50.00% Assessed Coursework

Level: 7

Sem A - Music Perception and Cognition

Credits: 15.0
Contact: Dr Marcus Pearce

Description: Music is a fundamental part of being human and exists only in the mind of the listener. This module will provide students with advanced training in current understanding of how musical sound is processed by the mind and brain. This is crucial for developing creative tools for musicians and intuitive interfaces for music lovers as well as for using technology in the creative production of new music.

Assessment:

  • Item 1: 70.00% Examination (centrally administered)
  • Item 2: 30.00% Assessed Coursework

Level: 7

Sem A - Introduction to Computer Vision

Credits: 15.0
Contact: Prof Andrea Cavallaro

Description: In recent years, research in computer vision has made significant progress. This is largely driven by the recognition that effective visual perception is crucial in understanding intelligent behaviour - unless we understand how we perceive, we will never understand how we reason The first part of the module will introduce the relevant concepts and techniques in machine learning. In the second part we will show how these techniques can be applied to various areas in computer vision.

Assessment:

  • Item 1: 50.00% Examination (centrally administered)
  • Item 2: 25.00% Assessed Coursework
  • Item 3: 25.00% Assessed Coursework

Level: 7

Sem A - Computer Graphics

Credits: 15.0
Contact: Dr Miles Hansard

Description: This course is concerned primarily with computer graphics systems and in particular 3D computer graphics. The course will include revision of fundamental raster algorithms such as polygon filling and quickly move onto the specification, modeling and rendering of 3D scenes. In particular the following topics may be covered: viewing in 2D,data structures for the representation of 3D polyhedra, viewing in 3D, visibility and hidden surface algorithms, illumination computations. Some attention will be paid to human perception of colour and interactive 3D such as virtual reality.

Assessment:

  • Item 1: 60.00% Examination (centrally administered)
  • Item 2: 10.00% Practical
  • Item 3: 10.00% Practical
  • Item 4: 10.00% Practical
  • Item 5: 10.00% Practical

Level: 7

Sem A - Multi-platform Game Development

Credits: 15.0
Contact: Dr Diego Perez-Liebana

Description: This module covers the fundamentals of game development in a multi-platform (consoles, PC, Web and mobile devices) environment). The course focuses on development of 3D games, covering all aspects of game development: the game loop, math, physics, audio, graphics, input, animations, particle systems and artificial intelligence. This module has a strong programming content, required for laboratories and assignments. The practical aspects will be taught using a popular game development platform. The main assignment of this module consists of the development of a full 3D game at the student's choice.

Assessment:

  • Item 1: 20.00% Assessed Coursework
  • Item 2: 70.00% Assessed Coursework
  • Item 3: 10.00% Examination/Test (not centrally administered)

Level: 7

Sem A - Artificial Intelligence in Games

Credits: 15.0
Contact: Dr Diego Perez-Liebana

Description: This module covers a range of Artificial Intelligence techniques employed in games, and teaches how games are and can be used for research in Artificial Intelligence. This module has a strong programming component. The module explores algorithms for creating agents that play classical board games (such as chess or checkers) and real-time games (Mario or PacMan), including single agents able to play multiple games. The module gives an overview of multiple techniques, such as Monte Carlo Tree Search, Evolutionary Computation, Deep and Machine Learning applied to games.

Assessment:

  • Item 1: 20.00% Assessed Coursework
  • Item 2: 40.00% Assessed Coursework
  • Item 3: 40.00% Assessed Coursework

Level: 7

Sem B - Music Informatics

Credits: 15.0
Contact: Prof Simon Dixon

Description: This module introduces students to state-of-the-art methods for the analysis of music data, with a focus on music audio. It presents in-depth studies of general approaches to the low-level analysis of audio signals, and follows these with specialised methods for the high-level analysis of music signals, including the extraction of information related to the rhythm, melody, harmony, form and instrumentation of recorded music. This is followed by an examination of the most important methods of extracting high-level musical content, sound source separation, and on analysing multimodal music data.

Assessment:

  • Item 1: 60.00% Examination (centrally administered)
  • Item 2: 20.00% Assessed Coursework
  • Item 3: 20.00% Assessed Coursework

Level: 7

Sem B - Music and Audio Programming

Credits: 15.0
Contact: Dr Andrew Mcpherson

Description: This module will introduce a broad class of principles of programming music and audio systems, with a particular focus on real-time digital signal processing on embedded hardware. Students will develop audio projects using the Bela embedded hardware platform, which is based on an ARM Cortex-A series processor, an architecture also commonly found in mobile devices. This is a project-based module, with the overall mark determined by two smaller assignments and one more extensive final project. It is expected that students already understand basic digital signal processing theory and have a moderate familiarity with programming in C, C++ or a similar language.

Assessment:

  • Item 1: 20.00% Assessed Coursework
  • Item 2: 30.00% Assessed Coursework
  • Item 3: 50.00% Assessed Coursework

Level: 7

Sem B - Deep Learning for Audio and Music

Credits: 15.0
Contact: Dr Quoc Huy Phan
Prerequisite: Before taking this module you must take ECS708P

Description: This module, for those who have some prior knowledge of machine learning, focusses on deep learning methods and how they can be used to address many tasks in audio and music. The theory of modern deep neural networks (DNNs) is covered, including training of common DNN types as well as modifying DNNs for new purposes. Various tasks in analysis/generation of audio and music are studied directly to inspire the content, using raw audio and/or symbolic representations. Background in machine learning is essential, and some background in digital signal processing is highly recommended. Music knowledge would be desirable but is not a requirement.

Assessment:

  • Item 1: 10.00% Practical
  • Item 2: 10.00% Examination/Test (not centrally administered)
  • Item 3: 40.00% Assessed Coursework
  • Item 4: 40.00% Examination (centrally administered)

Level: 7

Sem B - Deep Learning and Computer Vision

Credits: 15.0
Contact: Prof Shaogang Gong

Description: Fuelled by the advances in sensing, computing and Machine Learning, Computer Vision applications start finding their way in our everyday lives. Face detection/recognition in Facebook, augmented reality with Google glasses, gaming with Microsoft kinect, to name just a few. This module, covers emerging topics/applications in the field of Computer Vision, and the underlying Machine Learning methodologies.

Assessment:

  • Item 1: 50.00% Assessed Coursework
  • Item 2: 20.00% Practical
  • Item 3: 30.00% Assessed Coursework

Level: 7

Sem B - Computational Creativity

Credits: 15.0
Contact: Dr Michael Cook

Description: There will be two main areas of content for this module: (i) creative AI procedures and practice and (ii) philosophical issues of Computational Creativity. The first area will cover the application of well-known AI techniques such as Deep Learning and Markov Models to generative projects, as well as ad-hoc techniques. These will be illustrated with applications in music, the visual arts and video game design, considering issues of human-computer interaction in these domains. The second area will raise and discuss questions around the value of having autonomous and semi-autonomous creative AI systems in society, drawing on philosophy, sociology, psychology and cognitive science, as well as engineering disciplines.

Assessment:

  • Item 1: 100.00% Assessed Coursework

Level: 7

Interaction, Intelligence, and Sensing:

Sem A - Computer Programming

Credits: 15.0
Contact: Dr Fabrizio Smeraldi

Description: This module provides an introduction to the principles of programming in the context of designing and constructing complete programs. Programming techniques will be introduced and practical work will form an integral part of the course and of the assessment of students. The first half of the course will concentrate on program structures. The second half will cover representation of abstract types such as lists and trees using the types such as records and arrays provided in imperative programming languages.

Assessment:

  • Item 1: 70.00% Examination (centrally administered)
  • Item 2: 15.00% Assessed Coursework
  • Item 3: 15.00% Assessed Coursework

Level: 7

Sem A - Natural Language Processing

Credits: 15.0
Contact: Dr Julian Hough

Description: Natural Language Processing (aka Computational Linguistics) has become an important and growing field in the last decade. Many of the most important applications for computing now involve the processing and understanding of spoken or written language: machine translation, question answering, news summarisation, text and opinion mining, and spoken dialogue systems like the iPhone's Siri. This module will introduce the core techniques in language processing, including statistical and rule-based approaches, and show how to apply them to the main application areas.

Assessment:

  • Item 1: 40.00% Examination/Test (not centrally administered)
  • Item 2: 60.00% Assessed Coursework

Level: 7

Sem A - Database Systems

Credits: 15.0
Contact: Dr Tony Stockman

Description: Introduction to databases and their language systems in theory and practice.

The main topics covered by the module are:

The principles and components of database management systems.
The main modelling techniques used in the construction of database systems.
Implementation of databases using an object-relational database management system.
SQL, the main relational database language.
Object-Oriented database systems.
Future trends, in particular information retrieval and data warehouses.

There are 2 timetabled lectures a week, and 1 hour tutorial per week (though not every week). There will be timetabled laboratory sessions (2 hours a week) for approximately 4 weeks.

Assessment:

  • Item 1: 50.00% Assessed Coursework
  • Item 2: 50.00% Assessed Coursework

Level: 7

Sem A - Enabling Communication Technologies for IoT

Credits: 15.0
Contact: Dr Eliane Bodanese

Description: This module provides a comprehensive study of the major communication technologies that enable applications on Internet of Things. This module comes as a response to the increasing commercial and research interest in smart everywhere applications, like smart grid, smart city, smart home, industrial automation, telemetry, etc. This module covers the technologies that allow the formation of a network for autonomous communication and processing between devices that supply the vital information, such as sensing and identification for the smart applications . Topics include: Radio Frequency Identification (RFID); Near Field Communication (NFC); Wireless Sensor Networks: covering its major concepts in node sensing, wireless transmission characteristics, medium access protocols, and routing protocols; Wireless Personal Area Networks such as the ones using IEEE802.15.4 standard, Zigbee, Zwave; Low Power Wide Area Networks such as LoRa and Sigfox systems; and Power line communications.

Assessment:

  • Item 1: 70.00% Examination (centrally administered)
  • Item 2: 15.00% Assessed Coursework
  • Item 3: 15.00% Examination/Test (not centrally administered)

Level: 7

Sem A - Applied Statistics

Credits: 15.0
Contact: Prof Steve Uhlig

Description: The module introduces core statistical concepts for practical data analysis. It will provide students with the skills to model data sources, analyze their statistical properties, visualize them in different ways and fit the samples to a known probabilistic model.

Assessment:

  • Item 1: 60.00% Examination (centrally administered)
  • Item 2: 10.00% Assessed Coursework
  • Item 3: 10.00% Assessed Coursework
  • Item 4: 10.00% Assessed Coursework
  • Item 5: 10.00% Assessed Coursework

Level: 7

Sem A - Introduction to Software Engineering

Credits: 15.0
Contact: Dr Anne Hsu

Description: The main focus of this module is software engineering and systems analysis. Students will learn about system complexity and the special challenges of building software systems. They will learn how to analyse system and software requirements, produce object-oriented designs, and learn the principles of how to plan, manage and test systems. Content covers:

Systems Analysis
Requirements capture and analysis
Use cases; UML for use-cases
Object oriented design; UML for class diagrams
Project management
Software lifecycle
Quality assurance and testing

Assessment:

  • Item 1: 50.00% Assessed Coursework
  • Item 2: 50.00% Assessed Coursework

Level: 7

Sem A - Research Methods and Responsible Innovation

Credits: 15.0
Contact: Mr Gyorgy Fazekas

Description: This module will teach generic high-level research and transferable skills applicable to pure and applied research in computer science and engineering. The module fosters the development of practical understanding of established approaches, methods and techniques of research; conceptual understanding that enables critical and rigorous evaluation of research; ability to communicate ideas and conclusions logically and fluently in both written and oral contexts. It will also discuss responsible research and innovation practices, acknowledging that science can raise questions and dilemmas, is often ambiguous in terms of purposes and motivations and unpredictable in terms of impacts. Topics include research writing with an introduction to LaTeX, research ethics and responsible innovation, quantitative, qualitative and reproducible research methods, including experiment design and basic statistical analysis with an introduction to statistical programming and an introduction to scientific programming environments and version control systems.

Assessment:

  • Item 1: 35.00% Assessed Coursework
  • Item 2: 30.00% Assessed Coursework
  • Item 3: 35.00% Assessed Coursework

Level: 7

Sem A - Artificial Intelligence

Credits: 15.0
Contact: Dr Quoc Huy Phan

Description: This module provides an overview of techniques used in Artificial Intelligence including agent modelling, problem formulation, search, logic, probability and machine learning.

Assessment:

  • Item 1: 50.00% Examination (centrally administered)
  • Item 2: 25.00% Assessed Coursework
  • Item 3: 25.00% Assessed Coursework

Level: 7

Sem A - Electronic Sensing

Credits: 15.0
Contact: Dr Ildar Farkhatdinov

Description: The new module focuses on electronic engineering aspects of sensing and instrumentation systems. It integrates the themes of signal theory, metrology, sensing & transduction, signal acquisition and conditioning for further processing, analysis, characterisation and design of sensing electronic systems, system-level considerations and sensor data analysis techniques. The knowledge and skills developed through this module are essential for any student engaging in the design of systems which extract signals from, or interact with the real world, and are highly relevant to electronic engineers designing, testing and using sensing systems and applications.

Assessment:

  • Item 1: 50.00% Examination (centrally administered)
  • Item 2: 25.00% Assessed Coursework
  • Item 3: 25.00% Assessed Coursework

Level: 7

Sem A - Data Mining

Credits: 15.0
Contact: Dr Emmanouil Benetos

Description: Data that has relevance for decision-making is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the Internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and electronic patient records. Data mining is a rapidly growing field that is concerned with developing techniques to assist decision-makers to make intelligent use of these repositories. The field of data mining has evolved from the disciplines of statistics and artificial intelligence.

This module will combine practical exploration of data mining techniques with a exploration of algorithms, including their limitations. Students taking this module should have an elementary understanding of probability concepts and some experience of programming.

Assessment:

  • Item 1: 60.00% Examination (centrally administered)
  • Item 2: 10.00% Assessed Coursework
  • Item 3: 10.00% Assessed Coursework
  • Item 4: 10.00% Assessed Coursework
  • Item 5: 10.00% Assessed Coursework

Level: 7

Sem B - Interaction Design

Credits: 15.0
Contact: Dr Tony Stockman

Description: Traditionally, interactive systems design has focused on enhancing people's efficiency or productivity. For example, to increase the speed with which tasks can be completed or to minimise the number of errors people make. Economic and social changes have led to a situation in which the primary use of many technologies is for fun; ie. in which there is no quantifiable output and no clear goal other than enjoyment. Computer games, mobile music players and online communities are all examples where the quality of the experience is the primary aim of the interaction. This module explores the challenges these new technologies, and the industries they have created, present for the design and evaluation of interactive systems. It moves away from a human computer interaction model, which is too constrained for real world problems and provides you with an opportunity to engage with theories relating to cultural dynamics, social activity, and live performance. It explores the nature of engagement with interactive systems and between people when mediated by interactive systems.

Assessment:

  • Item 1: 50.00% Assessed Coursework
  • Item 2: 50.00% Assessed Coursework

Level: 6

Sem B - Digital Media and Social Networks

Credits: 15.0
Contact: Dr Mathieu Barthet

Description: Introduction to Online Social Networks (OSN)
Characteristics of OSNs
Basic Graph Theory
Small World Phenomenon
Information propagation on OSNs
Influence and Content Recommendation
Sentiment Analysis in Social Media
Privacy and ethics

Assessment:

  • Item 1: 6.00% Assessed Coursework
  • Item 2: 6.00% Assessed Coursework
  • Item 3: 6.00% Assessed Coursework
  • Item 4: 20.00% Assessed Coursework
  • Item 5: 6.00% Assessed Coursework
  • Item 6: 6.00% Assessed Coursework
  • Item 7: 50.00% Assessed Coursework

Level: 7

Sem B - Information Retrieval

Credits: 15.0
Contact: Dr Qianni Zhang

Description: The field of information retrieval (IR) aims to provide techniques and tools to support effective and efficient access to large amounts of textual information (e.g. stored on the web, digital libraries, intranets). This involves representation, retrieval, presentation and user issues.

The following topics will be covered:

1. Application of representation and retrieval approaches described in the Foundations of Information Retrieval module, Semester A, in the context of structured documents, in particular web documents, and digital libraries.

2. Databases & information retrieval, and logical models for information retrieval.

3. The organisation of documents according to categories (e.g. Yahoo directory) or their content to provide more effective presentation of the collection to the users.

4. The design of interfaces and visualisation tools that aim at supporting end-users in their search tasks.

5. User aspects, including the evaluation of IR systems according to user satisfaction, and the incorporation of user information seeking behaviour in the search task.

The module consists of 3 hours per week of lectures for 12 weeks, including labs and tutorials.

Assessment:

  • Item 1: 65.00% Examination (centrally administered)
  • Item 2: 10.00% Assessed Coursework
  • Item 3: 10.00% Assessed Coursework
  • Item 4: 15.00% Assessed Coursework

Level: 7

Sem B - Cognitive Robotics

Credits: 15.0
Contact: Dr Lorenzo Jamone

Description: This module addresses the emerging field of autonomous systems possessing artificial reasoning skills and also environment and context awareness. The module will introduce students to advance numerical and computational techniques associated with machine learning and artificial intelligence. Successfully-applied algorithms and autonomy models form the basis for study, and provide students an opportunity to design such a system as part of their coursework project. Theory and practical applications will be linked through discussion of real systems such as medical robotic surgeons and robotic musicians.

Assessment:

  • Item 1: 50.00% Examination (centrally administered)
  • Item 2: 10.00% Assessed Coursework
  • Item 3: 15.00% Examination/Test (not centrally administered)
  • Item 4: 25.00% Practical

Level: 7

Sem B - Robotics

Credits: 15.0
Contact: Dr Ranjan Vepa
Prerequisite: Before taking this module you must take DEN5109 and take DEN5200

Description: The module introduces robotics as an integral part of modern automation, provides an introductory insight into the engineering design and application of robot manipulator systems. It also provides an understanding of kinematics, dynamics and trajectory planning of robotic manipulators, actuators and sensors, principles and roles in robotics. It introduces various aspects of robot modelling and control and problems encountered in robot programming and their remedies.

Assessment:

  • Item 1: 70.00% Examination (centrally administered)
  • Item 2: 30.00% Assessed Coursework

Level: 7

Creative Industries:

Sem B - Business Information Systems

Credits: 15.0
Contact: Dr Mahesha Samaratunga

Description: The role of software is increasingly critical in our everyday lives and the accompanying risks of business or safety critical systems failure can be profound. This module will provide students with a framework for articulating and managing the risks inherent in the systems they will develop as practitioners. Likewise, students will learn how to build decision support tools for uncertain problems in a variety of contexts (legal, medical, safety), but with a special emphasis on software development. This course will make a distinctive offering that will enable our students to bring a principled approach to bear to analyse and solve uncertain and risky problems. Course contents: Quantification of risk and assessment: Bayesian Probability & Utility Theory, Bayes Theorem & Bayesian updating; Causal modelling using Bayesian networks with examples; Measurement for risk: Principles of measurement, Software metrics, Introduction to multi-criteria decision aids; Principles of risk management: The risk life-cycle, Fault trees, Hazard analysis; Building causal models in practice: Patterns, identification, model reuse and composition, Eliciting and building probability tables; Real world examples; Decision support environments.

Assessment:

  • Item 1: 20.00% Examination/Test (not centrally administered)
  • Item 2: 50.00% Assessed Coursework
  • Item 3: 30.00% Assessed Coursework

Level: 7

Sem B - Ethics, Regulation and Law in Advanced Digital Information Processing...

Full Title: Ethics, Regulation and Law in Advanced Digital Information Processing and Decision Making

Credits: 15.0
Contact: Dr Jialun Hu

Description: This module takes a practical approach to the coverage of ethics in Artificial Intelligence and Data Science. It sees ethical considerations as part of a spectrum of concerns, including ethics, but extending through regulation and legal compliance as formal expressions of what is and is not ethical. It considers examples of the kinds of issues that arise in existing systems, and uses the UK Government's Ethical Framework as an example of how to embed considerations of ethics into business processes.

Assessment:

  • Item 1: 30.00% Examination/Test (not centrally administered)
  • Item 2: 20.00% Assessed Coursework
  • Item 3: 50.00% Assessed Coursework

Level: 7

Core Module:

Sem B/C - MSc Project

Credits: 60.0
Contact: Dr Usman Naeem

Description: The aim of the MSc project is to give students the opportunity to apply to a significant advanced project, the techniques and technologies, that they have learned in their lecture modules. Projects will either be significantly development based, or else have a research focus. All projects will be expected either to investigate or to make use of techniques that are at the leading edge of the field. Candidates will be asked to submit a project report on completion of the allotted project period (3 months full time). This report will be evaluated using the standard criteria for scholarly work. Projects will also include a viva where students will be required to demonstrate and defend their work.

Assessment:

  • Item 1: 100.00% Assessed Coursework

Level: 7

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