Classification Basic concepts and evaluation Decision Trees Bayes Learning Linear Models Support Vector Machines Kernels Multiple classifiers Regression Linear and logistic regression Instance based (K-NN) Perceptron Neural networks Deep neural networks (CNN) Unsupervised learning Clustering (k-Means) Latent variables (EM) Reinforcement learning MDP Q-learning Deep Reinforcement Learning HMM POMDP

Machine Learning, Tom Mitchell. Pattern Recognition and Machine Learning, Chris Bishop. Machine Learning: a Probabilistic Perspective, Kevin Murphy. Introduction to Machine Learning, Ethem Alpaydin. Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville. Reinforcement Learning, Sutton and Barto.

- ML (6 CFU) Syllabus - ● Classificazione - Concetti di Base, Decision Trees, Bayes ● Apprendimento, Modelli Lineari, Support Vector Machines, Kernels, Multiple classifiers ● Regressione - Regressione Lineare e Logistica, Instance based (K-NN), ● Perceptron, Reti Neurali, Deep neural networks (CNN) ● Apprendimento non supervisionato - Clustering (k-Means), Variabili latenti (EM), ● Reinforcement learning - MDP, Q-learning - Reinforcement Learning (3 CFU Aggiuntivi) - ● Ragionamento Probabilistico ● Ragionamento Probabilistico su scala temporale ● MDPs e Bandit Problems ● Model-free reinforcement learning (Monte-Carlo, Temporal Difference, etc.) ● Nozioni di model-based reinforcement learning ● Metodi Policy Gradient e Actor-Critic

- Reinforcement Learning: Reinforcement Learning - An Introduction, II ed. Sutton & Barto - Probabilistic Reasoning: Artificial Intelligence: A Modern Approach, Russell & Norvig