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Computational Intelligence

Code 12878
Year 1
Semester S1
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 Main: -The PDFs of the theoretical classes. -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
Teaching Methodologies and Assessment Criteria This CU has both theoretical and prctical classes. In the theoretical classes the teacher presents the course materials and promotes its discussion with the students. In the practical classes, the students solve practical problems using the Python programming language. Theoretical tests (14 points - 70%) - 2 tests Practical Project (6 points - 30%) Presence in the 80% of the classes is required.
Language Portuguese. Tutorial support is available in English.
Last updated on: 2019-10-03

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