Overall objectives:

1 - The main objective of the course is to prepare the student to use Artificial Neural Networks as a tool in the development end research in different areas.

Syllabus:

1 - Introduction to Artificial Neural Networks.
2 - Artificial Neural Networks Topologies.
3 - Training Algorithms.
4 - System Identification with Artificial Neural Networks.
5 - Artificial Neural Networks models use for predictio, classification and Control.
6 - Other Artificial Intelligence Tools.
7 - Hardware Implementation.
8 - Real world case studies.
9 - Advanced Topics in Artificial Neural Networks.

Literature/Sources:

Magnus Nørgaard, Ole Ravn, Niels K. Poulsen and Lars K. Hansen , 2000 , Neural Networks for Modelling and Control of Dynamic Systems , Springer-Verlag, London
Martin T. Hagan, Howard B. Demuth e Mark Hudson Beale , 2002 , Neural Network Design , Publisher Martin Hagan
Morgado Dias , 2005 , Técnicas de controlo não-linear baseadas em Redes Neuronais: do algoritmo à implementação , Universidade de Aveiro
Mathworks , 2011 , Neural Network Toolbox , Mathworks
José C. Principe, Neil R. Euliano, W. Curt Lefebvre , 2000 , Neural and Adaptive Systems: Fundamentals through Simulations , Wiley

Assesssment methods and criteria:

Classification Type: Quantitativa (0-20)

Evaluation Methodology:
The evaluation will be done based in the assignments of simulation of Artificial Neural Networks. These assignments work as projects. Synthesis Assignment/Introductory Assignment - 20% Classification Assignment - 40% Regression Assignment - 40%