The goal of the course is to enable students to extract (“learn”) from measured data useful information for automation, systems and control applications. The course covers the fundamentals of data-driven learning, system identification and virtual sensing.  Each technique is treated both theoretically and practically through computer exercises with real-world data.

Through theoretical lectures and computer sessions, the students are expected to:

• understand the fundamental problems, shared by applied statistics, machine learning and system identification, that can be encountered while extracting information from a finite set of observations taken from an unknown system;
• learn the main tools of statistical learning and system identification;
• be able to formulate learning/identification problems corresponding to different applications;
• understand a range of algorithms along with their strengths and weaknesses;
• be able to apply suitable algorithms to solve application problems;
• be able to read current research papers and understand the issues raised by current research in the field.

The program of the course is as follows.

Part 1 (Statistical learning for Automation Systems):

• Introduction to statistical learning: main definitions and comparison with similar disciplines
• The mathematical foundations of learning
• Learning of static models
• Data-preprocessing
• Clustering
• Towards learning of dynamical models
• Virtual sensing: the Kalman filter

Part 2 (System Identification and Prediction):

• Discrete-time linear dynamical systems: definition and analysis of model classes for Time Series and Input/Output models (Box-Jenkins models, and sub-classes like ARX and ARMAX)
• Linear optimal prediction
• Black-box identification from measured data-sets of Input/Output models
• Black-box identification from measured data-sets of state-space models
• Gray-box system-identification using Extended Kalman Filter
• Extension to non-linear dynamical systems of black-box system-identification

• Familiarity with basic concepts of computer science (algorithms and complexity) and dynamical systems theory.
• Mathematical maturity in linear algebra and probability theory.

The exam will be a written test in one of the official available dates. The exam will consist of both theoretical questions and simple  (i.e., no computer-aided) exercises. The final marks will be awarded based on correctness of the answers, appropriateness of technical terminology and clarity of exposition. Specifically, the students will be asked to:

• show their understanding of the main problems (and the corresponding countermeasures) in the field of data-driven modeling and learning in general;
• select and describe the most appropriate tools for a given application;
• use analytical methods to solve learning problems of moderate complexity.

Freely available online.

Freely available online.

Freely available online.