• a: Write the net_t(x) of a preceptron with inputs i \in [1, ..., k]
    b: Write the radial basis function kernel
  • Answer true or false to the following and motivate your answer
    a: In a SVM can the alpha values help to select the best features? False (they only select some input patterns and not the components)
    b: To estimate the (future) predictive capability of your model is it a good practice to consider the result and accuracy obtained by the model selection pahse without looking to the training results? True
    c: Increasing the VC-dim, th VC-bound on the risk R (according to SLT) increases. True
  • Write the derivation of the bias-variance decomposition (assuming without proving the variance lemma)
  • Show a picture of:
    a: undercomplete autoencoder
    b: overcomplete autoencoder