The course is offered on the II semester of the I year. The course is taught in English. The course addresses the core issues in machine learning, with a special focus on algorithms and theory of statistical machine learning, and modern techniques for deep learning. Lab activities will quip students with first-hand experience on modern optimization methods and programming framework most used in advance research and companies as of today, and to have first hand experiences on the properties of such algorithms on specific case studies.

- Knowledge of the main characteristics of artificial Intelligence: historical overview and modern definition; role of Machine and deep learning - Knowledge of the main characteristics of Statistical Machine Learning: theory and algorithms - Knowledge of the main characteristics of Artificial Neural Networks - Knowledge of the main characteristics of modern Deep Learning techniques

Probability

- Artificial Intelligence: historical definition, brief overview, modern definition and current role of machine and deep learning. - Overview of fundamental knowledge of probability. - Generative and discriminative methods. - Perceptron. - Artificial Neural Networks. - Support Vector Machines. - Kernel functions. - Active learning - Convolutional Neural Networks. - Stochastic Gradiant Descent. - Batch Normalization. - Generative Adversarial Networks - Recurrent Neural Networks. - Learning theory. - Data bias - Learning to learn

The course includes practices on the lecture topics and laboratory sessions during which the students will form team and work on specific projects assigned to them. Laboratory sessions allow experimental activities on the most widespread commercial and open-source products.

Copies of the slides used during the lectures will be made available. All teaching material is downloadable from the teaching Portal. Reference books: - I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT press. - K. P. Murphy. Machine Learning: a probabilistic perspective. MIT Press

Modalità di esame: Prova orale obbligatoria; Prova scritta tramite PC con l'utilizzo della piattaforma di ateneo; Elaborato progettuale in gruppo;

The exam will consist of a written test, a practical project in group and the presentation and discussion of the project of the group, with a Q&A session

Modalità di esame: Prova orale obbligatoria; Prova scritta tramite PC con l'utilizzo della piattaforma di ateneo; Elaborato progettuale in gruppo;

The exam will consist of a written test, a practical project in group and the presentation and discussion of the project of the group, with a Q&A session