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Délia Boino
Submitted by dboino on 3 April 2021
Intended learning outcomes
  1. Be able to construct models applied to solving concrete problems;
  2. From a mathematical model, to be able to build a computational implementation in an appropriate language and from this to carry out efficient simulations;
  3. Be able to understand the vulnerabilities of the model and correct their inadequacies, to criticize the solutions found and realize how they can be improved;
  4. Understand the distinction between Bayesian inference and classical inference and apply the first to prediction and classification problems;
  5. To be able to construct graphical models in order to characterize the structure of dependencies of a problem and, from these, to produce simulations;
  6. Be able to understand the functioning of a neural network and its learning algorithms;
  7. Distinguish the various types of neural networks and their characteristic features;
  8. Be able to follow generically the technological advances in this area and realize the new challenges posed.


Curricular Unit Form