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SCC.361: Artificial Intelligence


Department: Computing and Communications (School of) NCF Level: FHEQ/QCF/NQF6//RQF6
Study Level: Part II (yr 3) Credit Points: 15.0
Start Date: 09-10-2017 End Date: 15-12-2017
Available for Online Enrolment?: Y Enrolment Restriction: Only available to students majoring in delivering department
Module Convenor: Dr MCM Lau

Syllabus Rules and Pre-requisites

  • Prior to SCC.361, the student must have successfully completed:

Curriculum Design: Outline Syllabus

    • Introduction to Artificial Intelligence, including a historical background;
    • Knowledge Representation and Reasoning: Predicate logic and logical inference, Knowledge based systems, Intelligent Systems, operations over fuzzy sets, fuzzy sets, fuzzy models (Mamdani and Takagi-Sugeno type), fuzzy inference;
    • Searching and planning, Decision Making (DM), DM under uncertainties
    • Probability theory: Bayesian Decision Theory. Probabilistic inference, Bayes networks;
    • Machine Learning: Supervised and unsupervised learning – principles of learning, learning a class from examples, definition and levels of autonomy, generalisation, over-fitting, regularisation, statistical, probabilistic and rule-based methods. Introduction to Neural Networks and Decision Trees.
    • Introduction to Multivariate Methods and Clustering and Classification approaches.
    • Introduction to evolutionary algorithms, genetic algorithms, phenotype, genotype, basic genetic operators
    • Introduction to programming languages suitable for intelligent systems (e.g. Scheme, Prolog)
    • Applications of Artificial Intelligence

Curriculum Design: Pre-requisites/Co-requisites/Exclusions

  • SCC110 Software Development

Curriculum Design: Single, Combined or Consortial Schemes to which the Module Contributes

    • BSc Computer Science
    • BSc Computer Science Innovation
    • BSc IT for Creative Industries
    • BEng Communication Systems and Electronics
    • BEng Computer Systems Engineering
    • BSc Management and IT
    • BSc/ BA Computer Science and  European Languages
    • BSc/ BA Computer Science and Music
    • BSc/ BA Computer Science and Mathematics
    • BSc/BA Accounting, Finance and Computer Science
    • BSc Natural Sciences
  • 60% Exam
  • 40% Coursework

Assessment: Details of Assessment

  • The assessment will be based on practical tests (totalling 40%) and the exam (60%). The combination of formative coursework assessment and summative examination provides opportunities for the students to demonstrate their skills and theoretical knowledge. The practicals will comprise two exercises (contributing 20% each) to be undertaken and completed independently. The formal examination will last 2.5 hours and require answering 3 out of 4 questions.

Educational Aims: Subject Specific: Knowledge, Understanding and Skills

  • This module aims to give students a broad grounding in artificial intelligence including knowledge and understanding of reasoning, decision making, fuzzy logic, neural networks, and genetic algorithms and the skills to implement artificial intelligent systems. The understanding gained through the module should give the students an appreciation of the challenges in this area. Furthermore, the module will prepare students to understand and critically analyse artificial intelligence techniques used in modern computers and mobile devices.

Educational Aims: General: Knowledge, Understanding and Skills

  • This fundamental computer science module aims more generally to be aware of the requirements of artificial intelligence systems in general and place these in the context of computing and communications systems. The broad grounding in knowledge based, probabilistic and logical systems through machine learning techniques should encourage the students to be more aware of competing approaches more widely in other aspects of their studies.

Learning Outcomes: Subject Specific: Knowledge, Understanding and Skills

  • At the end of the module the students should be able to

    •  Understand fundamental Artificial Intelligence concepts and current trends and issues;
    • Understand and apply a range of artificial intelligence techniques including neural networks, fuzzy logic-based reasoning, genetic algorithms, search and planning;
    • Know of applications of artificial intelligence to intelligent systems;
    • Recognise computational problems suited to an ‘intelligent’ solution and design intelligent systems;
    Apply the above techniques to analyse and design intelligent systems.

Learning Outcomes: General: Knowledge, Understanding and Skills

  • On successful completion of this module students will be able to:
    • Reflect more productively on problems where multiple sources of information are used to define a solution
    •  Explain and justify design choices where information is not fully defined

     

     

Contact Information

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