Advanced Computer Science with a Year in Industry MSc

  • Programme duration: Full-time: 24 months  
  • Programme start: September 2020
  • Entry requirements: You will need a 2:1 Honours degree (or above) in a subject area closely related to Computer Science, Economics, or the intersection of these two subject areas.
Advanced Computer Science with Year in Industry msc

Module details

Compulsory modules

Research Methods in Computer Science (COMP516)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting0:100
Aims

1. To provide a deep and systematic undersanding of the nature and conduct of CS research. 2. To enhance existing transferable key skills. 3. To develop high order transferable key skills. 4. To equip students with the ability to undertake independent research. 5. To remind students of the Legal, Social, Ethical and Profesional (LSEP) issues applicable to the computer industry.

Learning Outcomes

(LO1) Have an understanding of how established techniques of research and enquiry are used to extend, create and interpret knowledge in Computer Science.

(LO2) Have a conceptual understanding sufficient to:(i) evaluate critically current research and advanced scholarship in Computer Science and (ii) propose possible alternative directions for further work.

(LO3) Be able to: (i) deal with complex issues at the forefront of the academic discipline of Computer Science in a manner, based on sound judgements, that is both systematic and creative, (ii) demonstrate self-direction and originality in tackling and solving problems within the domain of Computer Science, (iii) act autonomously in planning and implementing solutions in a professional manner and (iv) define, plan, and/or carry out a project related to research and to communicate conclusions clearly to both specialists and non-specialists.

(LO4) Make use of the qualities and transferable skills necessary for employment requiring:(i) the exercise of initiative and personal responsibility, (ii) decision making in complex and unpredictable situations, (iii) scientific risk identification, assessment and control, and (iv) the independent learning ability required for continuing professional development.

(LO5) Understand and participate within the professional, legal, social and ethical framework within which they would be expected to operate as professionals within the IT industry.

(LO6) Have the skills set to be able to continue to advance their knowledge and understanding, and to develop new skills to a high level, with respect to continuing professional development as a "self-directed life-long learner" across the discipline of Computer Science.

(S1) Communication (oral, written and visual) - Presentation skills – oral

(S2) Communication (oral, written and visual) - Listening skills

(S3) Communication (oral, written and visual) - Academic writing (inc. referencing skills)

(S4) Time and project management - Project planning

(S5) Research skills - Ethical awareness

MSc Project (COMP702)
LevelM
Credit level60
SemesterSummer (June-September)
Exam:Coursework weighting0:100
Aims

To give students the opportunity to work in a guided but independent fashion to explore a substantial problem in depth, making practical use of principles, techniques and methodologies acquired elsewhere in the programme.
To give experience of carrying out a large piece of individual work and in producing a dissertation.
To enhance communication skills, both oral and written.

Learning Outcomes

(LO1) After completing the module students should be able to: Investigate and specify a substantial problem in the domain of Computer Science, to place it in the context of related work including, as appropriate, Computer Science reserach, and to produce a plan to address this problem

(LO2) Make use of the qualities and transferable skills necessary for the conduct of a Computer Science project: (i) the exercise of initiative and personal responsibility, (ii) decision making in complex situations, (iii) risk identification (including, as appropriate, commercial and scientific risk), assessment and control, and (iv) the independent learning ability required for continuing professional development

(LO3) Demonstrate effective time management, self-direction and originality in carrying out a project in the domain of Computer Science

(LO4) Locate and make use of information relevant to a given IT project

(LO5) Design a solution to a substantial IT problem

(LO6) Implement and test potential solutions to IT problems

(LO7) Evaluate critically, as relevant to the project, current research and advanced scholarship in Computer Science, evaluate their own work, and participate effectively in the process of peer review of other projects

(LO8) Conduct and evaulate critically the project within the professional, legal, social and ethical framework in Computer Science and Sortware Engineering

(LO9) Prepare and deliver formal presentations

(LO10) Prepare and deliver a demonstration of software

(LO11) Structure and write a dissertation describing their project

(S1) Communication (oral, written and visual) - Presentation skills – oral

(S2) Communication (oral, written and visual) - Presentation skills - written

(S3) Communication (oral, written and visual) - Academic writing (inc. referencing skills)

(S4) Time and project management - Project planning

(S5) Critical thinking and problem solving - Critical analysis

(S6) Critical thinking and problem solving - Evaluation

(S7) Commercial awareness - Ability to analyse/balance risk and reward

Optional modules

Knowledge Representation (COMP521)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting75:25
Aims

To introduce Knowledge Representation as a research area.
To give a complete and critical understanding of the notion of representation languages and logics.
To study modal logics and their use.
To study description logic and its use.
To study epistemic logic and its use.
To study methods for reasoning under uncertainty

Learning Outcomes

(LO1) Demonstrate a critical understanding of the languages of modal and description logics by translating between English and those languages.

(LO2) Exhibit a comprehensive understanding of the semantics of modal and description logics by arguing whether formulas of propositional, modal and description logic are true or valid.

(LO3) Analyse scenarios involving knowledge, and represent them in modal and description logics.

(LO4) Have a deep understanding of formal proof methods and apply them to modal and description logics.

(S1) Problem Identification

(S2) Critical Analysis

(S3) Solution Synthesis

(S4) Evaluation of Problems and Solutions

Privacy and Security (COMP522)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting0:24
Aims

The aims of this module are: to introduce students to the major problems and solution approaches in the area of computer and Internet privacy, confidentiality and security. to provide a theoretical framework for subsequent research in these challenging areas.

Learning Outcomes

(LO1) At the end of the module, students should understand the main problems in security, confidentiality and privacy in conputers and in networks, and the reasons for their importance.

(LO2) At the end of the module, students should understand the main approaches adopted for their solution and/or mitigation, together with the strengths and weaknesses of each of these approaches

(LO3) At the end of the module, students should understand the main encryption algorithms and protocols

(LO4) At the end of the module, students should appreciate the application of encryption algorithms to secure messaging, key distribution and exchange, authentication and electronic payment systems

(LO5) At the end of the module, students should understand the use of epistemic logics for formal modeling of security and privacy protocols.

(LO6) At the end of the module, students should understand the legal and ethical issues related to securit, confidentiality and privacy.

(S1) Adaptability

(S2) Problem solving skills

(S3) Numeracy

(S4) IT skills

(S5) Commercial awareness

(S6) Ethical awareness

(S7) Lifelong learning skills

Advanced Algorithmic Techniques (COMP523)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting0:24
Aims

To provide a sound foundation concerning the design and analysis of advanaced discrete algorithms.
To provide a critical rational concerning advanced complexity theory and algorithmics.
To provide an in-depth, systematic and critical understanding of selected significant issues at the forefront of research explorations in the design and analysis of discrete algorithms.

Learning Outcomes

(LO1) Describe the following classes of algorithms and design principles associated with them: recursive algorithms, graph (search-based) algorithms, greedy algorithms, algorithms based on dynamic programming, network flow (optimisation) algorithms, approximation algorithms, randomised algorithms, distributed and parallel algorithms.

(LO2) Illustrate the above mentioned classes by examples from classical algorithmic areas, current research and applications.

(LO3) Identify which of the studied design principles are used in a given algorithm taking account of the similarities and differences between the principles.

(LO4) Apply the studied design principles to produce efficient algorithmic solutions to a given problem taking account of the strengths and weaknesses of the applicable principles.

(LO5) Outline methods of analysing correctness and asymptotic performance of the studied classes of algorithms, and apply them to analyse correctness and asymptotic performance of a given algorithm.

(S1) Critical thinking and problem solving - Critical analysis

(S2) Critical thinking and problem solving - Evaluation

(S3) Critical thinking and problem solving - Problem identification

(S4) Critical thinking and problem solving - Creative thinking

(S5) Numeracy/computational skills - Reason with numbers/mathematical concepts

(S6) Numeracy/computational skills - Problem solving

Biocomputation (COMP305)
Level3
Credit level15
SemesterFirst Semester
Exam:Coursework weighting0:20
Aims

To introduce students to some of the established work in the field of neural computation.

To highlight some contemporary issues within the domain of neural computation with regard to biologically-motivated computing particularly in relation to multidisciplinary research.

To equip students with a broad overview of the field of evolutionary computation, placing it in a historical and scientific context.

To emphasise the need to keep up-to-date in developing areas of science and technology and provide some skills necessary to achieve this.

To enable students to make reasoned decisions about the engineering of evolutionary ('selectionist') systems.

Learning Outcomes

(LO1) Account for biological and historical developments neural computation

(LO2) Describe the nature and operation of MLP and SOM networks and when they are used

(LO3) Assess the appropriate applications and limitations of ANNs

(LO4) Apply their knowledge to some emerging research issues in the field

(LO5) Understand how selectionist systems work in general terms and with respect to specific examples

(LO6) Apply the general principles of selectionist systems to the solution of a number of real world problems

(LO7) Understand the advantages and limitations of selectionist approaches and have a considered view on how such systems could be designed

(S1) Improving own learning/performance - Reflective practice

(S2) Improving own learning/performance - Self-awareness/self-analysis

(S3) Critical thinking and problem solving - Critical analysis

(S4) Critical thinking and problem solving - Evaluation

(S5) Critical thinking and problem solving - Synthesis

(S6) Critical thinking and problem solving - Problem identification

(S7) Critical thinking and problem solving - Creative thinking

(S8) Research skills - All Information skills

(S9) Research skills - Awareness of /commitment to academic integrity

(S10) Numeracy/computational skills - Numerical methods

(S11) Numeracy/computational skills - Problem solving

(S12) Skills in using technology - Information accessing

Geographic Data Science (ENVS563)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting0:100
Aims

The module provides students with core competences in Geographic Data Science (GDS). This includes the following:

Advancing their statistical and numerical literacy;

Introducing basic principles of programming and state-of-the-art computational tools for GDS;

Presenting a comprehensive overview of the main methodologies available to the Geographic Data Scientist, as well as their intuition as to how and when they can be applied;

Focusing on real world applications of these techniques in a geographical and applied context.

Learning Outcomes

(LO1) Demonstrate advanced GIS/GDS concepts and be able to use the tools programmaticallyto import, manipulate and analyse data in different formats.

(LO2) Understand the motivation and inner workings of the main methodological approcahes ofGDS, both analytical and visual.

(LO3) Critically evaluate the suitability of a specific technique, what it can offer and how it canhelp answer questions of interest.

(LO4) Apply a number of spatial analysis techniques and how to interpret the results, in theprocess of turning data into information.

(LO5) When faced with a new data-set, work independently using GIS/GDS tools programmatically.

(S1) Numeracy

(S2) Organisational skills

(S3) Problem solving skills

(S4) IT skills

(S5) Ethical awareness

(S6) Communication skills

Image Processing (ELEC319)
Level3
Credit level7.5
SemesterFirst Semester
Exam:Coursework weighting100:0
Aims

To introduce the basic concepts of digital image processing and pattern recognition.

Learning Outcomes

(LO1) After successful completion of the module, the student should have: An understanding of main principles of digital image processing, and its relation to pattern recognition in images, object detection,  tracking and machine vision. An appreciation of the areas of applications for various image enhancement techniques.

(LO2) After successful completion of the module, the student should have: An understanding of the standard methods of image manipulation, representation and information extraction.

(S1) On successful completion of the module, students should be able to show experience and enhancement of the following key skills: Independent learning Problem solving and design skills

(S2) After successful completion of the module, the student should have: The ability to apply relevant image enhancement techniques to a given problem. The necessary mathematical skills to develop standard image processing algorithms.

Multi-core and Multi-processor Programming (COMP528)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting0:40
Aims

To provide students with a deep, critical and systematic understanding of key issues and effective solutions for parallel programming for systems with multi-core processors and parallel architectures.
To develop students appreciation of a variety of approaches to parallel programming, including using MPI, OpenMP and CUDA. 
To develop the students skills in parallel programming using MPI, OpenMP and CUDA.
To develop the students skills in parallelization of existing serial code.

Learning Outcomes

(LO1) At the end of the module students should be able to:  Explain the concepts of multi-core processors and systems and parallel architectures,  their advantages and challenges of their programming.

(LO2) Appraise the differences between various programming techniques and programming patterns available for parallel programming for multi-core systems and parallel architectures. 

(LO3) Design parallel multi-threaded programs using the most appropriate for a particular application approach, using one of MPI, OpenMP, CUDA, or a combination of thereof.  

(LO4) Design and implement reasonably sophisticated parallel multi-threaded programs demonstrating reasonable scalability on multi-core and parallel systems  using MPI, OpenMP, or CUDA, or the combination of thereof. 

(LO5) Analyse and evaluate the efficiency and scalability of parallel multi-threaded programs for multi-core parallel systems.

(S1) Improving own learning/performance - Personal action planning

(S2) Communication (oral, written and visual) - Presentation skills

(S3) Communication (oral, written and visual) - Report writing

(S4) Critical thinking and problem solving

(S5) Information skills - Critical reading

(S6) Numeracy/computational skills - Reason with numbers/mathematical concepts

(S7) Numeracy/computational skills - Problem solving

Optimisation (COMP557)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting0:25
Aims

To provide a foundation for modelling various continuous and discrete optimisation problems.

To provide the tools and paradigms for the design and analysis of algorithms for continuous and discrete optimisation problems. Apply these tools to real-world problems.

To review the links and interconnections between optimisation and computational complexity theory.

To provide an in-depth, systematic and critical understanding of selected significant topics at the intersection of optimisation, algorithms and (to a lesser extent) complexity theory, together with the related research issues.

Learning Outcomes

(LO1) The ability to recognise potential research opportunities and research directions

(LO2) The ability to read, understand and communicate research literature in the field of optimisation.

(LO3) The ability to use appropriate algorithmic paradigms and techniques in context of a particular optimisation model.

(LO4) The ability to formulate optimisation models for the purpose of modelling particular applications.

(LO5) A critical awareness of current problems and research issues in the field of optimisation.

(S1) Critical thinking and problem solving - Critical analysis

(S2) Communication (oral, written and visual) - Presentation skills – oral

Applied Algorithmics (COMP526)
LevelM
Credit level15
SemesterSecond Semester
Exam:Coursework weighting75:25
Aims

The main aim of this module is to lay down a strong context for research explorations in the field of algorithms. This is done through a rigorous study of selected algorithmic solutionswith application to related fields requiring analysis of large data (bioinformatics, networking, data compression, etc). This will be done by provision of the rationale for the use of algorithmic design and analysis methods, and also an in-depth, systematic and critical study of several important algorithmic challenges residing on the border of the theory of abstract algorithms and engineering of applied algorithmic solutions.

Learning Outcomes

(LO1) Critical awareness of algorithmic problems and as well as research issues in the context of engineering of efficient algorithmic solutions.

(LO2) Clear understanding of the relation (including differences) between the goals in the design of efficient abstract and applied algorithmic solutions.

(LO3) Ability to understand and assimilate research literature relating to the application of algorithmic techniques.

(LO4) Ability to undertake small software projects.

(LO5) Ability to communicate (within and outside of Algorithms/CS community) problems related to efficiency of algorithmic solutions

(S1) Critical thinking and problem solving - Critical analysis

(S2) Critical thinking and problem solving - Problem identification

(S3) Critical thinking and problem solving - Evaluation

(S4) Critical thinking and problem solving - Creative thinking

(S5) Numeracy/computational skills - Problem solving

Ontologies and Semantic Web (COMP318)
Level3
Credit level15
SemesterSecond Semester
Exam:Coursework weighting80:20
Aims

To provide guidelines, concepts and models for designing and evaluating applications utilising advanced web technologies To introduce Artificial Intelligence and Semantic Web techniques which can be applied to the application of advanced web technologies To introduce the notion of semantic web applications intended to be used by software.

Learning Outcomes

(LO1) At the conclusion of the module students shouldHave an understanding of the basic formal methods and techniques for designing and implementing advanced web applications

(LO2) Have an appreciation for Artificial Intelligence and Semantic Web research related to advanced web technology applications

(LO3) Be able to apply specific methods and techniques in the design and development of an application of advanced web technology for a case study

(S1) Information skills - trustability of information sources

(S2) Numeracy/computational skills - Problem solving

(S3) Information skills - Critical thinking

Algorithmic Game Theory (COMP559)
LevelM
Credit level15
SemesterSecond Semester
Exam:Coursework weighting75:25
Aims

1. To provide an understanding of the inefficiency arising from uncontrolled, decentralized resource allocation.
2. To provide a foundation for modelling various mechanism design problems together with their algorithmic aspects.
3. To provide the tools and paradigms for the design and analysis of efficient algorithms/mechanisms that are robust in environments that involve interactions of selfish agents.
4. To review the links and interconnections between algorithms and computational issues with selfish agents.
5. To provide an in-depth, systematic and critical understanding of selected significant topics related to algorithmic game theory, together with the related research issues.

Learning Outcomes

(LO1) Have a critical awareness ofcurrent problems, important concepts and research issues in  the field ofalgorithmic game theory. 

(LO2) Systematic knowledge andability to quantify the inefficiency of equilibria.

(LO3) Systematic knowledge andability to formulate mechanism design models or network games for the purpose of modeling particularapplications.

(LO4) Detailed understanding andability to use, describe and explain appropriate algorithmic paradigms and techniques in context of aparticular game-theoretic or mechanism design problem.

(LO5) Critical ability to read,understand and communicate research literature in the field of algorithmic game theory.  

(LO6) Critical ability torecognise potential research opportunities and research directions in the field of algorithmic game theory.

(S1) Communication (oral, written and visual) - Presentation skills – oral

(S2) Communication (oral, written and visual) - Presentation skills - written

(S3) Communication (oral, written and visual) - Presentation skills - visual

(S4) Critical thinking and problem solving - Critical analysis

(S5) Information skills - Critical reading

(S6) Business and customer awareness

(S7) Computer science principles

Data Mining and VIsualisation (COMP527)
LevelM
Credit level15
SemesterSecond Semester
Exam:Coursework weighting75:25
Aims

To provide an in-depth, systematic and critical understanding of some of the current research issues at the forefront of the academic research domain of data mining.

Learning Outcomes

(LO1) A critical awareness of current problems and research issues in Data Mining.

(LO2) A comprehensive understanding of current advanced scholarship and research in data mining and how this may contribute to the effective design and implementation of data mining applications.

(LO3) The ability to consistently apply knowledge concerning current data mining research issues in an original manner and produce work which is at the forefront of current developments in the sub-discipline of data mining.

(LO4) A conceptual understanding sufficient to evaluate critically current research and advanced scholarship in data mining.

(S1) Critical thinking and problem solving - Problem identification

(S2) Critical thinking and problem solving - Critical analysis

Information Theory and Coding (ELEC415)
LevelM
Credit level7.5
SemesterSecond Semester
Exam:Coursework weighting100:0
Aims

To introduce the techniques used in source coding and error correcting codes, including the use of information as a measure.

Learning Outcomes

(LO1) After successful completion of the module the student should have: An appreciation of information sources and of the information rates    available on real channels. An appreciation of techniques for making the best use of channels for efficient transmission with error protection.

(LO2) After successful completion of the module the student should have: An understanding of the basic methods of source coding and error correcting codes.

(S1) Critical thinking and problem solving - Critical analysis

(S2) Critical thinking and problem solving - Evaluation

(S3) Critical thinking and problem solving - Problem identification

Machine Learning and Bioinspired Optimisation (COMP532)
LevelM
Credit level15
SemesterSecond Semester
Exam:Coursework weighting75:25
Aims

In this module we focus on learning agents that interact with an initially unknown world. Since the world is dynamic this module will put strong emphasis on learning to deal with sequential data unlike many other machine learning courses. The aims can be summarised as:
To introduce and give an overview to state of the art bio-inspired self-adapting methods. 
To enable students to not only learn to build models with reactive input/output mappings but also build computer programs that sense and perceive their environment, plan, and make optimal decisions. 
To familiarise students with multi-agent reinforcement learning, swarm intelligence, deep neural networks, evolutionary game theory, artificial immune systems and DNA computing.
To demonstrate principles of bio-inspired methods, provide indicative examples, develop problem-solving abilities and provide students with experience to apply the learnt methods in real-world problems.

Learning Outcomes

(LO1) A systematic understanding of bio-inspired algorithms that can be used for autonomous agent design and complex optimisation problems.

(LO2) In depth insight in  the mathematics of biologically inspired machine learning and optimisation methods.

(LO3) A comprehensive understanding of the benefits and drawbacks of the various methods.

(LO4) Demonstrate knowledge of using the methods in real-world applications (e.g. logistic problems).

(LO5) Practical assignments will lead to hands on experience using tools as well as coding of own algorithms.

Multi-agent Systems (COMP310)
Level3
Credit level15
SemesterSecond Semester
Exam:Coursework weighting100:0
Aims

To introduce the student to the concept of an agent and multi-agent systems, and the main applications for which they are appropriate.

To introduce the main issues surrounding the design of intelligent agents.

To introduce the main issues surrounding the design of a multi-agent society.

To introduce a contemporary platform for implementing agents and multi-agent systems.

Learning Outcomes

(LO1) Understand the notion of an agent, how agents are distinct from other software paradigms (eg objects) and understand the characteristics of applications that lend themselves to an agent-oriented solution; Understand the key issues associated with constructing agents capable of intelligent autonomous action, and the main approaches taken to developing such agents; Understand the key issues in designing societies of agents that can effectively cooperate in order to solve problems, including an understanding of the key types of multi-agent interactions possible in such systems Understand the main application areas of agent-based solutions, and be able to develop a meaningful agent-based system using a contemporary agent development platform.

Reasoning About Action and Change (COMP525)
LevelM
Credit level15
SemesterSecond Semester
Exam:Coursework weighting75:24
Aims

1. Give the student a feel for several formalisms that deal with change
2. Show how logics can be used to specify and verify dynamic systems
3. Give students a deeper knowledge of the semantics of such systems
4. Develop awareness of the usual trade-off between expressivity and complexity of logical languages.

Learning Outcomes

(LO1) Provide formal specifications, using a logical language, of informal problem descriptions.

(LO2) Verify simple properties of models.

(LO3) Produce simple logical proofs.

(LO4) Understand how temporal logics relate to each other.

(LO5) Understand and use model checkers.

(LO6) Understand and be able to explain and fomulate properties (such as "safety", "fairness" and "liveness") of systems and be able to formulate simple instances of them.

(S1) Numeracy/computational skills - Reason with numbers/mathematical concepts

(S2) Numeracy/computational skills - Problem solving

Safety and Dependability (COMP524)
LevelM
Credit level15
SemesterSecond Semester
Exam:Coursework weighting75:24
Aims

1. To provide a critical and in-depth understanding of all aspects of software safety and dependability; including issues realting to security, reliability and trustworthiness.
2. To provide a broad understanding of the state-of-the-art software engineering techniques currently used to address safety and dependability issues.
3. To provide an overview of the contemporary research issues relating to software safety and dependability.

Learning Outcomes

(LO1) At the end of the module, a student will understand some of the problems associated with the use of computer software in critical applications where safety, security and trust are issues.

(LO2) At the end of the module, a student will understand some of the contemporary mechanisms for ensuring dependability and reliability.

(LO3) At the end of the module, a student will understand a variety of approaches to the design and development of safe and dependable systems.

(LO4) At the end of the module, a student will understand formal verification techniques in relation to the assessment of safety and dependability.

(LO5) At the end of the module, a student will  be aware of some of the contemporary research problems in the areas of safety, security, dependability and trust.

(S1) Working in groups and teams - Group action planning

(S2) Skills in using technology - Using common applications (work processing, databases, spreadsheets etc.)

(S3) Numeracy/computational skills - Reason with numbers/mathematical concepts

(S4) Numeracy/computational skills - Problem solving

(S5) Communication skills - Presenting

(S6) Problem solving - Co-designing a program and a correctness proof

(S7) Problem solving - Model (MDP) design analysis

(S8) Business and customer awareness - Brief discussion of the cost of software bugs and the cost of applying formal techniques

(S9) Information Technology (IT) skills - Formulating (probabilistic) models as Markov chains and decision processes, and using of-the-shelf tools for their analysis

Technologies for E-commerce (COMP315)
Level3
Credit level15
SemesterSecond Semester
Exam:Coursework weighting100:0
Aims

To introduce the environment in which e-commerce takes place, the main technologies for supporting e-commerce, and how these technologies fit together.

To introduce security as a major issue in secure e-commerce, and to provide an overview of security issues.

To introduce encryption as a means of ensuring security, and to describe how secure encryption can be delivered.

To introduce issues relating to privacy.

To introduce auction protocols and negotiation mechanisms as emerging e-commerce technologies.

Learning Outcomes

(LO1) Upon completing this module, a student will: understand the main technologies behind e-commerce systems and how these technologies interact; understand the security issues which relate to e-commerce; understand how encryption can be provided and how it can be used to ensure secure commercial transactions; understand implementation aspects of e-commerce and cryptographic systems; have an appreciation of privacy issues; and understand auction protocols and interaction mechanisms.

Web Mapping and Analysis (ENVS456)
LevelM
Credit level15
SemesterSecond Semester
Exam:Coursework weighting0:100
Aims

The module has two main aims. First, it seeks to provide hands-on experience and training in the design and generation of web-based mapping and geographical information tools. Second, it seeks to provide hands-on experience and training in the use of software to access, analyse and visualize web-based geographical information.

Learning Outcomes

(LO1) Experience using tile rendering tools to generate content for map-based web sites.

(LO2) Web-based data collection techniques

(LO3) Programming skills to enable basic online data manipulation and web mapping

(LO4) Knowledge of web based mapping infrastructure

(S1) Communication skills

(S2) Problem solving skills

(S3) IT skills

(S4) Organisational skills

(S5) Numeracy

Computational Intelligence (COMP575)
Level1
Credit level15
SemesterSecond Semester
Exam:Coursework weighting100:0
Aims

Understand the basic structures and the learning mechanisms underlying neural networks within the field of artificial intelligence and examine how synaptic adaptation can facilitate learning and how input to output mapping can be performed by neural networks. Obtain an overview of linear, nonlinear, separable and non separable classification as well as supervised and unsupervised mapping. Understand the benefit of adopting naturally inspired techniques to implement optimisation of complex systems and acquire the fundamental knowledge in various evolutionary techniques. Become familiar with the basic concepts of systems optimisation and its role in natural and biological systems and entities.

Learning Outcomes

(LO1) Learning  the advantages and main characteristics of neural networks in relation to traditional methodologies. Also, familiarity with different neural networks structures and their learning mechanisms.

(LO2) Appreciation of the advantages of evolutionary-related approaches for optimisation problems and their advantages compared to traditional methodologies. Also, understanding the different techniques of evolutionary optimisation for discrete and continuous configurations

(LO3) Understanding of the needs for genetic encoding and modelling for solving optimisation problems and familiarisation with the evolutionary operators and their performance.

(LO4) Understanding of the neural network learning processes and their most popular types, as well as  appreciation of how neural networks can be applied to artificial intelligence problems.

(S1) On successful completion of this module the student should be able to pursue further study in artificial intelligence as well as more advanced types of neural networks and evolutionary optimisation and bio-inspired techniques.

(S2) On successful completion of this module the student should be able to analyse numerically the mathematical properties of most major network types and apply them to artificial intelligence problems. Also, the student should be able to appreciate and understand the suitability of evolutionary optimisation in systems where classical methods cannot be effective.

(S3) On successful completion of this module the student should be able to approach methodologically artificial intelligence problems and bio-inspired algorithms in general and understand the principal mathematics of learning systems and the fundamental principles governing evolutionary optimisation techniques.


No more than 2 level 3 modules may be taken in both semesters

Students normally take 60 credits in each semester. However, if you opt to select the two 7.5 credit modules, you will have an uneven distribution of credits across the two semesters.

Note: in exceptional circumstances, and with the approval of the programme Director of Studies, alternative modules available within the Computer Science provision may be substituted for optional and required modules, except COMP516 and COMP702