Statistical Learning II

4 credits

Linear discriminants; the Perceptron; the margin and large margin classifiers; learning theory; empirical vs. structural risk minimization; the VC dimension; kernel functions; reproducing kernel Hilbert spaces; regularization theory; Lagrangian optimization; duality theory; the support vector machine; boosting; Gaussian processes; applications. Recommended preparation: ECE 109. Prerequisites: ECE 271A with a grade of B– or above; graduate standing.