Course Description

This course provides a comprehensive view of machine learning and neural networks,
starting from multilayer perceptrons to advanced architectures currently used
(deterministic/probabilistic networks, convolutive networks (CNN), and recurrent networks (RNN).
We survey the fundamentals of neural network algorithms, and we introduce several properties that aid in the selection of the most appropriate architectures of networks depending on the task at hand.