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Computational Intelligence

Computational Intelligence

Code	11495
Year	1
Semester	S2
ECTS Credits	6
Workload	PL(30H)/T(30H)
Scientific area	Informatics
Entry requirements	None.
Mode of delivery	Face-to-face
Work placements	Not applicable.
Learning outcomes	Introduce the concepts, models and language adequate to problem resolution using computational intelligence. At the end of this course unit the student should be able to solve problems using the techniques of computational intelligence. At the end of this course students should be able to: -LO1: Characterize Computational Intelligence, its approaches, fundamentals and applications; -LO2: Develop computational applications using the techniques taught in this course.
Syllabus	1-Neural networks 1.1-The artificial neuron 1.2-Supervised learning 1.3-Practical issues regarding supervised learning 1.4-Unsupervised learning 2-Evolutionary computation 2.1-Genetic algorithms 2.2-Genetic programming 2.3-Evolutionary strategies 2.4-Coevolution 3-Swarm intelligence 3.1-Particle swarm optimization 3.2-Ant colony optimization 4-Fuzzy systems 4.1-Fuzzy systems 4.2-Fuzzy inference 4.3-Fuzzy control
Main Bibliography	The PDFs of the theoretical classes. Main: -Andries P. Engelbrecht, Computational Intelligence, An Introduction, John Wiley & Sons, 2002. Complementary: -S. Haykin, Neural Networks: A Comprehensive Foundation, 2ª ed., Prentice Hall, 1998 -T. Bäck, D.B. Fogel, A. Michalewicz, Handbook of Evolutionary Computation, IOP Publishers and Oxford University Press, 1997 -J. Kennedy, R.C. Eberhart, Y. Shi, Swarm Intelligence, Morgan Kaufmann Publishers, 2001 -E. Cox, The fuzzy systems handbook: A practitioner's guide to building, using and maintaining fuzzy systems, Morgan Kaufmann Publishers, 1999
Language	Portuguese. Tutorial support is available in English.