Content
1) Foundations of inductive learning: empirical risk minimization, structural risk minimization, Bayesian inference
2) Learning and generalisation: gradient-based optimization, overfitting-underfitting, regularisation, application to regression and classification problems
3) Artificial neural networks (connectionist neurons, multilayer perceptrons, radial basis functions, deep networks, recurrent networks)
4) Aspects of computational sustainability, of biases, and fairness in machine learning algorithms
5) Statistical learning theory and support vector machines
6) Probabilistic methods and graphical models: reasoning under uncertainty, Bayesian learning for neural networks
7) Reinforcement Learning (MDP, value iteration, policy iteration, Q-learning)
