The aim of this course is that the student knows and develops computational learning techniques in the context of Artificial Neural Networks in addition designing and implementing applications and systems that use them, including those dedicated to automatic extraction of information and knowledge from data.
In more detail, the competences acquired by students are:
- Knowledge (PO: a, e, k)
-To know the mathematical / biological foundations of artificial neural neurons.
-Acquiring the concept of neural network and learning process.
-To know the different architectures of neural networks.
-To know the different learning paradigms of neural networks and their theoretical foundation.
-To know the differences among different types of neural networks from an applied perspective.
-To understand the operation of artificial neural networks, adapting each technique to the specific characteristics of problem.
-To know the different areas of applicability of artificial neural networks.
- Application (PO: b, d, e, g, k)
-To apply knowledge of neural networks in solving real problems, with emphasis on the accuracy and complexity of models.
-To identify correctly the different phases for solving a problem using neural networks.
-To develop an application that solves approximation, prediction or classification problems using neural networks.
-Ability to design a set of experiments that lead to solving the problem.
-To document correctly solving a problem using neural networks.
- Analysis, synthesis and evaluation (PO: b, e)
-Ability to analyze and interpret results.
-To recognize and classify the different problems that can be solved by artificial of neural networks.
-To combine and extrapolate the knowledge acquired for the design of a neural network, deciding the architecture and their parameters.
-Ability to assess the effectiveness of neural networks for solving a specific problem.
-To consider the relationship between computational cost and improvement of different solutions, choosing reasonable solutions to the characteristics of a given problem.