Neural and Evolutionary Learning
Objectives
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.
General characterization
Code
200294
Credits
4.0
Responsible teacher
Leonardo Vanneschi
Hours
Weekly - Available soon
Total - Available soon
Teaching language
Portuguese. If there are Erasmus students, classes will be taught in English
Prerequisites
TBD.
Bibliography
Teaching method
Use of (black-)board and slides in the theoretical classes. Use of Python computer programming environments for the practical classes.
Evaluation method
TBD
Subject matter
- A quick revision of Evolutionary Algorithms
- A deeper look at Genetic Programming
- What can be evolved? The importance of representation
- Evolutionary Computation as a meta-learning algorithm
- A quick revision of the functioning of artificial neurons
- A look back at the Backpropagation
- From gradient descent to the Backpropagation: a step-by-step derivation of the algorithm
- Limitations of the Backpropagation
- How to Improve the Backpropagation?
- Neuro-evolution: Neural Networks evolved by means of Genetic Programming
- Comparison between Backpropagation and Neuro-evolution: pros and cons
- A hint on model’s interpretability and interpretable AI
Programs
Programs where the course is taught: