An introductory course on learning systems in the context of signal processing,
artificial intelligence and control. Problems of classification, regression and
clustering. Neural networks: multi-level perceptrons and radial basis functions.
Decision trees. Elements of the learning theory: the Bayesian approach, hypothesis spaces.
Dimensionality reduction using principal components. Classification using
support vector machines. Reinforcement learning.
