• How does a machine learn a concept on the basis of examples?
  
• How can a neural network, after training, correctly predict the outcome of a previously unseen input?
  
• How much training is required to achieve a given level of accuracy in the prediction?
  
• How can one identify the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite time?
  

• support vector machines;
  
• fat-shattering dimensions and applications to neural network learning;
  
• learning with dependent samples generated by a beta-mixing process;
  
• connections between system identification and learning theory;
  
• probabilistic solution of 'intractable problems' in robust control and matrix theory using randomized algorithms.
  

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