IGD - Interaction, Graphics & Design Track

Introductory level class on Machine Learning on generic data: Supervised Learning, Unsupervised Learning, Kernels, Neural Networks, Basics of Deep learning and Graph neural network.

This course selects a number of advanced topics to explore in machine learning and autonomous agents, in particular: Probabilistic graphicsal models (Bayesian networks), Multi-output and structured-output prediction problems, deep-learning architectures, Methods of search and optimization, Sequential prediction and decision making, Reinforcement learning.

This course will present some basic learning algorithms for robots : reinforcement learning and imitation learning. Students will have the chance to implement and try some of these algorithms on simple examples. Very wide application fields in cognitive robotics and social robotics will be presented.