1. Início
  2. NOVA Information Management School
  3. Mestrado em Ciência de Dados e Métodos Analíticos Avançados
  4. Especialização em Business Analytics
  5. Neural and Evolutionary Learning

Neural and Evolutionary Learning

Objetivos

Transferring to the students knowledges about the possible ways in which Evolutionary Computation can help improving the performance of other Machine Learning methods, with particular reference to Artificial Neural Networks.

Caracterização geral

Código

200294

Créditos

4.0

Professor responsável

Leonardo Vanneschi

Horas

Semanais - A disponibilizar brevemente

Totais - A disponibilizar brevemente

Idioma de ensino

Português. No caso de existirem alunos de Erasmus, as aulas serão leccionadas em Inglês

Pré-requisitos

TBD.

Bibliografia

Método de ensino

Use of (black-)board and slides in the theoretical classes. Use of Python computer programming environments for the practical classes.

Método de avaliação

TBD

Conteúdo

  1. A quick revision of Evolutionary Algorithms
  2. A deeper look at Genetic Programming
  3. What can be evolved? The importance of representation
  4. Evolutionary Computation as a meta-learning algorithm
  5. A quick revision of the functioning of artificial neurons
  6. A look back at the Backpropagation
  7. From gradient descent to the Backpropagation: a step-by-step derivation of the algorithm
  8. Limitations of the Backpropagation
  9. How to Improve the Backpropagation?
  10. Neuro-evolution: Neural Networks evolved by means of Genetic Programming
  11. Comparison between Backpropagation and Neuro-evolution: pros and cons
  12. A hint on model’s interpretability and interpretable AI