| Código |
14473
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| Ano |
1
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| Semestre |
S2
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| Créditos ECTS |
6
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| Carga Horária |
PL(30H)/T(30H)
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| Área Científica |
Informática
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Mode of delivery |
Face-to-face
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Work placements |
Not applicable.
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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.
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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
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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.
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Main Bibliography |
The PDFs of the theoretical classes.
Main:
-Andries P. Engelbrecht, Computational Intelligence, An Introduction, John Wiley & Sons, 2007.
Complementary:
-Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, 2016.
-Janusz Kacprzyk, Witold Pedrycz, Handbook of Computational Intelligence, Springer, 2015.
-J. Kennedy, R.C. Eberhart, Y. Shi, Swarm Intelligence, Morgan Kaufmann Publishers, 2001
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| Language |
Portuguese. Tutorial support is available in English.
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