12858 (v.3) Computer Technology 403

Area:

Department of Electrical and Computer Engineering

Credits:

25.0

Contact Hours:

4.0

Lecture:

1 x 2 Hours Weekly

Tutorial:

1 x 1 Hours Fortnightly

Laboratory:

1 x 3 Hours Fortnightly

Prerequisite(s):

12855 (v.3) Embedded Software Design 304 or any previous version

Syllabus:

Fundamental issues in intelligent systems - History of artificial intelligence, Fundamental definitions, modelling the world, the role of heuristics. Search and Constraint Satisfaction - Problem spaces, search techniques, constraint satisfaction. Knowledge Representation and Reasoning - review of proposition and predicate logic, resolution and theorem proving, probabilistic reasoning, Bayes theorem. Advanced Search - genetic algorithms, simulated annealing, local search. Machine Learning and Neural Networks - definitionand examples of machine learning, supervised learning, learning decision trees, learning neural networks, learning theory, the problem of overfitting, unsupervised learning. Robotics - overview, state of the art, planning vs reactive control, uncertainty in control, sensing, world models, configuation space, planning, sensing, robot programming, navigation and control. Data Mining - the usefulness of data mining, associative and sequential patterns, data clustering, market basket analysis, data cleaning, data visualisation.

Unit Outcomes:

On successful completion of this unit students will have gained an introduction to the basics of computational intelliigent systems and an appreciatiation of the potential of applying and developing 'advanced' intelligent systems. They will learn how toutilise IS (intelligent systems) to solve engineering problems using the related technologies of Expert Systems, Computational Intelligence, Machine Learning, Autonomous Robots, Knowledge management and Data mining.

Text and references listed above are for your information only and current as of September 30, 2003. Please check with the unit coordinator for up-to-date information.

Unit References:

No prescribed references.

Unit Texts:

Negnevitsky, M., 2002, 'Artificial Intelligence - A Guide to Intelligent Systems', Addison-Wesley, Harlow.

Unit Assessment Breakdown:

Extended Abstract 5%. Research Paper 15%. Project presentation and paper 10%. Workbook on Project Development 5%. Tutorial Questions 15%. Final Examination 50%.

Year	Location	Period	Internal	Area External	Central External
2004	Bentley Campus	Semester 1	Y