- Knowledge and understanding of the presented techniques (detailed knowledge of the mathematics behind them; be acquainted of the limitations of the various techniques; awareness of the structural problem as e.r. the curse of dimensionality). - Practical application of the acquired knowledge (ability to identify the applicability domain of the various techniques with respect of the nature of data; ability to extract information from real and simulated data by applying the learned techniques via software application or development).

The students are assumed to know the topics covered by standard courses in mathematics given in the Bs.D. in Engineering. Furthermore: a good knowledge in basic probability and statistics is required: pdf, normal, expectation, mean, variance-covariance; gradient and conjugate gradient methods will be taken as granted.

- What Is AI? The Foundations of Artificial Intelligence; The History of Artificial Intelligence; The State of the Art. - Revision of Linear Algebra, Probability, Statistics and Optimization. - What Is Statistical Learning? Supervised, Unsupervised, Reinforcement and Semi-supervised Learning: examples. The Trade-Off Between Prediction Accuracy and Model Interpretability. Assessing Model Accuracy. Measuring the Quality of Fit. Validation and Cross-Validation. The Bias-Variance Trade-Off. - Metric and topological spaces. Curse of dimensionality; The Law of Large Numbers; the Geometry of High Dimensions: properties of the Unit Ball; Random Projection and Johnson-Lindenstrauss Lemma. - SVD and its application to Principal Components Analysis and Fisher Discriminant Analysis. - Classification Using a Separating Hyperplane. The Maximal Margin Classifier. Construction of the Maximal Margin Classifier. The Non-separable Case. Support Vector Classifiers. Support Vector Machines. Kernel Methods, representer and Mercer¿s theorems. - Basics of game theory; Multi-agent systems; Adversarial Learning; Reinforcement Learning: Q-learning, Nash equilibria.

Lessons, exercise classes and laboratory sessions will be given. There will be three hours of lesson per week plus one hour and half of exercises / laboratories.

Stuart J. Russell, Stuart Jonathan Russell, Peter Norvig, Ernest Davis Prentice Hall, 2010 An Introduction to Statistical Learning with Applications in R JAMES GARETH; WITTEN DANIELA; HASTIE TREVOR; TIBSHIRANI ROBERT Data Science and Machine Learning: Mathematical and Statistical Methods Dirk P. Kroese, Zdravko I. Botev, Thomas Taimre, Radislav Vaisman CRC Press, 2019 - 510 pagine

Lecture slides; Exercises; Lab exercises; Lab exercises with solutions; Video lectures (current year); Video lectures (previous years);

You can take this exam before attending the course

Exam: Written test; Compulsory oral exam; Individual essay; Group essay;

L'obiettivo dell'esame ¿ l'accertamento della conoscenza dei contenuti teorici del corso e della capacit¿ di declinarli nell'analisi dei dati. L'esame consiste in due parti: dapprima si dovr¿ realizzare una relazione, detta informalmente "tesina", anche in gruppi di massimo tre persone, sull'analisi di un dataset messo a disposizione dai docenti durante il corso, ed eseguita usando i metodi appresi a lezione. La tesina sar¿ presentata in una prova orale di circa 15 minuti durante la quale saranno anche poste domande sui contenuti di tipo teorico presentati nel corso. Il voto finale sar¿ deciso a valle della prova orale tenendo in considerazione: la qualit¿ della tesina, della sua esposizione, e il livello di comprensione delle tematiche teorico-modellistiche emerso dal colloquio.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.