School of Mathematical & Computer Sciences Dept of Computer Science



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Year 3, Semester 2





Course Code:

F29PD

Course Title:

Professional Development

Course Co-ordinator:

Sandy Jean-Jacques Louchart



Pre-requisites:

F28IN1 Interaction Design, F28IT1 Internet & Communications, F28DM2 Database Management Systems, F28SD2 Software Design, or equivalent

Aims:

    • To instil a professional and ethical attitude toward the application of computer technology

    • To introduce methods for the rational resolution of ethical problems

    • To provide an appreciation of the relevant professional and legal requirements concerning computer-based systems

    • To ensure an awareness of, and encourage deliberation about, the social implications of information technology



Syllabus:

  • Professionalism - British Computer Society.

  • Rules & Regulations - Codes & Standards; Computer Law; Ethical Decision Making.

  • Risks & Threats - Computer Crime; Viruses.

  • Privacy & Security – Databases; Biometrics.

  • Dependence & Change - Safety-Critical Systems; Technology & Society.

  • Brave New Worlds - Co-operative Computing; eLife.

Learning Outcomes:

Subject Mastery

Understanding, Knowledge and Subject-Specific Skills


  • British Computer Society Codes - Conduct; Practice

  • ISO & BSI Standards - Safety; Quality; Security

  • Statute Law - Contracts, Torts, Restitution; Data Protection; Freedom of Information, Intellectual Property; Computer Misuse

  • Ethics - Frameworks; Decision Making

  • Development life-cycle of a software system

  • Bi-directional influence between technological and societal trends

  • Current concerns over the application of computer technology

  • Current and potential remedies to abuse of computer technology




Learning Outcomes::

Personal Abilities:

Cognitive skills, Core skills and Professional Awareness


  • Practice in personal decision making and introspection

  • Identification and analysis of justification of personal choices to others

  • Critical analysis of rational reasoning, consequential reasoning and debate

  • Practice and reflective analysis of communication skills using a variety of media

  • Practice in working in a group, negotiating requirements, reaching a consensus, and working with others to a deadline

Assessment Methods:


Assessment:

Group Project: (weighting - 50%)

Examination: (weighting – 50%)

Synoptic with F29SO Software Engineering



Re-assessment:

Examination (weighting – 100%)





Course Code:

F29KM

Course Title:

Knowledge Management


Course Co-ordinator:

Jenny Coady

Pre-requisites:

None

Aims:

  • To provide students with an overview of information and knowledge management in organisations

  • To critically evaluate a range of methods used to develop strategies for information and knowledge management

  • To examine the role that knowledge and users play in the learning organisation

  • To critically evaluate the value of knowledge and IT for competitive advantage

Syllabus:

  • Information and Knowledge Management in Organisations

  • Principles of Knowledge Management: information mapping and information audits

  • Knowledge elicitation and representation

  • Information strategy development

  • Knowledge and IT for competitive advantage and as a corporate resource

  • The learning organisation

  • Planning for Knowledge Management within an organisation and the ethical issues which arise

Learning Outcomes:

Subject Mastery

Understanding, Knowledge and Subject-Specific Skills

  • Differentiating between Data, Information and Knowledge

  • Understand and evaluate theories and practices of knowledge management in organisations

  • Critically evaluate the value of knowledge and IT for competitive advantage

  • Compare and contrast methods to develop strategies and planning for knowledge management within organisations

  • Examine the rise of the concept of the Learning organisation and how it can aid in competitive advantage

Learning Outcomes::

Personal Abilities:

Cognitive skills, Core skills and Professional Awareness

  • Evaluating policies and strategies

  • Planning for large scale organisations

  • Ability to manage directed reading with self research (PDP)

  • Report writing and demonstrating argument development (PDP)

  • Use of technology as a means of learning, contributing and discussing (PDP)

Assessment Methods:


Assessment:

Examination: (weighting – 100%)



Re-assessment:

Examination: (weighting – 100%)







Course Code:

F29AI

Course Title:

Artificial Intelligence


Course Co-ordinator:

Ruth Aylett, Lilia Georgieva,

Patricia Vargas

Pre-requisites:

Elementary knowledge of logic at the level of undergraduate Computer Science. Knowledge of high-level programming language concepts

Aims:

  • To introduce the fundamental concepts and techniques of AI, including planning, search and knowledge representation

  • To introduce the scope, subfields and applications of AI, topics to be taken from a list including natural language processing, expert systems, robots and autonomous agents, machine learning and neural networks, and vision.

  • To develop skills in AI programming in an appropriate language

Syllabus:

  • Search algorithms (depth first search, breadth first search, uniform cost search, A* search)

  • constraint satisfaction problems;

  • games (min-max, alpha-beta pruning);

  • logic, resolution, introductory logic programming

  • knowledge representation – logic, rules, frames

  • goal and data-driven reasoning

  • practical rule-based programming

  • Overview of main fields of AI (Vision, Learning, Knowledge Engineering)

  • In depth view of one field of AI (e.g. Planning, Natural language)

  • Autonomous agents

  • Applications of AI

  • AI programming

Learning Outcomes:

Subject Mastery

Understanding, Knowledge and Subject-Specific Skills

  • Critical understanding of traditional AI problem solving and knowledge representation methods

  • Use of knowledge representation techniques (such as predicate logic and frames).

  • Critical understanding of different systematic and heuristic search techniques

  • Practice in expressing problems in terms of state-space search

  • Broad knowledge and understanding of the subfields and applications of AI, such as computer vision, machine learning and expert systems.

  • Detailed knowledge of one subfield of AI (e.g. natural language processing, planning) and ability to apply its formalisms and representations to small problems

  • Detailed understanding of different approaches to autonomous agent and robot architectures, and the ability to critically evaluate their advantages and disadvantages in different contexts.

  • Practice in the implementation of simple AI systems using a suitable language

Learning Outcomes::

Personal Abilities:

Cognitive skills, Core skills and Professional Awareness

  • Identification, representation and solution of problems

  • Time management and resource organization

  • Research skills and report writing

  • Practice in the use of ICT, numeracy and presentation skills.

Assessment Methods:


Assessment:

Examination: (weighting – 100%)




Re-assessment:

Examination: (weighting – 100%)






Course Code:

F28IN

Course Title:

Interaction Design


Course Co-ordinator:

Sandy Louchart

Pre-requisites:

F27IS1 Interaction Systems or equivalent

Aims:

The course aims to give students the opportunity to develop:

  • A broad knowledge and understanding of requirements gathering, design and evaluation theory and techniques in interaction design.

  • An introduction to commonly used design techniques and pattern for user interfaces.

  • A selection of routine skills and methods involved in working with users.

Syllabus:

Current topics in Interaction Design including: interaction design lifecycles, user interface design patterns, basic qualitative and quantitative data gathering and presentation techniques, accessibility.


Learning Outcomes:

Subject Mastery

Understanding, Knowledge and Subject-Specific Skills


  • Critically analyse interaction design and interfaces.

  • Propose solutions in response to interface design problems

  • Evaluate the effectiveness of user interfaces with respect to user requirements.




Learning Outcomes::

Personal Abilities:

Cognitive skills, Core skills and Professional Awareness


  • Use discipline appropriate software for data analysis,

  • Present, analyse and interpret simple numerical and graphical data gathered as part of evaluation studies. (PDP)

  • Communicate effectively to knowledgeable audiences by preparing informal presentations and written reports. (PDP)

  • Exercise autonomy and initiative by planning and managing their own work within a specified project; (PDP)

  • Take responsibility for their own and other’s work by contributing effectively and conscientiously to the work of a group (PDP)




Assessment Methods:


Assessment:

Examination: (weighting – 100%)



Re-assessment:

Examination: (weighting – 100%)





Course Code:

F27EM

Course Title:

Emerging Technologies


Course Co-ordinator:

Peter King, Rob Pooley


Pre-requisites:

None

Aims:

  • To explore emerging technologies through a variety of project work

Syllabus:

Mixed groups carrying out 4 projects, each of 3 weeks in duration.

Projects will vary, but the following are typical projects:

  • Controlling robots

  • Programming mobile/hand-held devices

  • Games

  • Ant based systems (biologically inspired computing)

  • Data-mining

Learning Outcomes:

Subject Mastery

Understanding, Knowledge and Cognitive Skills Scholarship, Enquiry and Research (Research-Subject Mastery Informed Learning)

  • The ability to carry out basic background research in a defined area

  • Active exploration of a problem domain within the department’s research portfolio

  • Develop problem solving strategies which are applicable across domains.

  • Reporting achievement

Learning Outcomes::

Personal Abilities:

Industrial, Commercial & Professional Practice Autonomy, Accountability & Working with Others Communication, Personal Abilities Numeracy & ICT

  • Report writing

  • Presentation skills

  • Skills for working in larger groups

  • Time management

  • Peer evaluation (where applicable)

Assessment Methods:


Assessment:

Examination: (weighting – 100%)



Re-assessment:

Examination: (weighting – 100%)





Course Code:

F29OC

Course Title:

Operating Systems & Concurrency


Course Co-ordinator:

To be confirmed

Pre-requisites:

F28DA1 Data Structures & Algorithms, F28PL2 Programming Languages or equivalent

Aims:

For the Operating Systems part: To provide an introduction to operating systems, their basic principles and shell programming.
For the Concurrency part: To introduce the theory and practice of concurrent hardware and software systems



Syllabus:

For the Operating Systems part: overview on operating systems concepts and structures, processes, threads, classical inter-process communication problems, Linux shell scripting.
For the Concurrency part: Process and Threads, Concurrent Execution, Shared Objects and Mutual Exclusion, Monitors and Condition Synchronisation, Deadlock, Safety and Liveness, Model Based Design. Performance, Introductions, Processors, Pipelines.



Learning Outcomes:

Subject Mastery

Understanding, Knowledge and Subject-Specific Skills
For the Operating Systems part:

  • Understanding of the concepts and structures present in modern operating systems.

  • Ability to write Linux shell scripting.


For the Concurrency part:

  • Broad and integrated knowledge and understanding of concurrency concepts, techniques and problems

  • Critical understanding of exclusion, synchronisation and deadlock

  • Detailed knowledge of abstract modelling and model-based design




Learning Outcomes::

Personal Abilities:

Cognitive skills, Core skills and Professional Awareness


    • Critically evaluate the problematic and concepts related to operating systems.

    • Analysis of the different possible solutions to the problematic.




Assessment Methods:


Assessment:

Examination (weighting – 100%)




Re-assessment:

Examination: (weighting – 100%)




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