Machine learning, classification and Bayesian network

This course aims to train the students in machine learning techniques:

• Overview of various ML techniques. Supervised learning (decision tree learning, k-nn, SVM) and unsupervised learning.
• Experiments with Weka, a data mining tool
• Bayesian reasoning and learning
• Project on reinforcement learning: the students must develop an agent that learns to escape a maze with Q-learning.