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  4. Sistemas Inteligentes

Sistemas Inteligentes

Objectives

The course will present artificial intelligence techniques for extracting usefull knowledge from data.
 

General characterization

Code

100097

Credits

6.0

Responsible teacher

Mauro Castelli

Hours

Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Prerequisites

No

Bibliography

Machine Learning. Tom Mitchell; Genetic programming: on the programming of computers by means of natural selection. J. Koza; 0; 0; 0

Teaching method

Theoetical classes and practical classes.
In the practical classes students will implement the algorithms presented in the theoretical classes. 

Evaluation method

First epoch: project (30%) and oral exam (70%)

Subject matter

Optimization problems: definition of problem and instance of a problem
Search space, neighborhood structure and related concept
No free lunch theorem
Local search
Simulated annealing
Genetic algorithms
Genetic Programming
Semantic genetic programming
Pareto dominance 
Multi objective optimization (NSGA II)