The course, optional for the Master degree in computer engineering, is offered on the I semester of the II year. The course is taught in English. The course addresses the core issues in modern Artificial intelligence and machine learning, with a special focus on advanced algorithms and theory of shallow and deep machine learning. Lab activities will equip 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 - Knowledge of how to formalize a learning problem from the mathematical foundations of the optimization objectives to the composition of a deep architecture - Knowledge of the main characteristics of modern deep learning techniques with practical engineering tricks for end-to-end training and fine-tuning the networks - Basic knowledge about how to deal with research questions, how to organize a project and prepare a scientific conference report

- Linear Algebra - Probability theory concepts - Basic concepts of decision theory (model optimization) - Python: Basic elements

- Artificial Intelligence: historical definition, brief overview, modern definition and current role of machine and deep learning - Overview of fundamental knowledge of probability - Overview of decision theory: loss, risk, Probably Approximately Correct (PAC) Learning - Perceptron - Support Vector Machines beyond classification - Artificial Neural Networks - Convolutional Neural Networks basic algorithms: Backpropagation and Stochastic Gradient Descent - Training a CNN (data preprocessing, weight initialization and hyperparameter optimization) - Visualizing and Understanding the CNN inner working - Multi-Task and Structured Output Learning (Semantic Segmentation and Detection) - Unsupervised and Metric Learning (Siamese networks and Contrastive Learning for Retrieval) - Generative Networks (Autoencoders and GANs) - Recurrent Neural Networks - Learning with few samples and across domains - Active and Incremental learning - Basic concepts and networks for Reinforcement Learning

- 48 hours of lectures (theory section) - 16 hours of 'laboratory' exercises (practical section)

Slides + State of the art deep learning top conference and journal papers. Optional - specific pointers to chapters of the following books will be indicated during the lectures. Artificial Intelligence: A Modern Approach S. Russell, P.Norvig Deep Learning by I. Goodfellow, Y. Bengio, A. Courville. Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David Machine Learning: a Probabilistic Perspective by Kevin P. Murphy The Elements of Statistical Learning, T. Hastie, R. Tibshirani, and J. Friedman Pattern Recognition and Machine Learning, Christopher M. Bishop Machine Learning, Tom M. Mitchell A Course in Machine Learning, Hal Daumé III Neural Networks - a Systematic Introduction, Raul Rojas

Modalitą di esame: Prova orale obbligatoria; Elaborato progettuale individuale; Elaborato progettuale in gruppo;

- individual homework reports - final project - oral exam on project presentation and on the theory topics presented during lectures

Modalitą di esame: Prova scritta (in aula); Elaborato progettuale individuale; Elaborato progettuale in gruppo;

- individual homework reports - final project - written exam on topics presented during lectures