The course introduces students to the design of A.I. based solutions to complex pattern recognition problems and discusses how to realize applications exploiting machine learning techniques. The course also presents fundamentals of signal and image processing. Particular focus will be given to pattern recognition problems and models dealing with sequential and visual data.
• Signal processing and time-series analysis
• Image processing, filters and visual feature detectors
• Bayesian learning and deep learning for machine vision and signal processing
• Deep learning for pattern recognition on non-vectorial data (physiological data, sensor streams, etc)
• Adaptive methods for graphs and relational data
• Reinforcement learning and intelligent agents
• Pattern recognition applications: machine vision, bio-informatics, robotics, medical imaging, etc.
• ML and deep learning libraries overview: e.g. Keras, Pytorch, Tensorflow, Ray, ...
A final project will introduce students to the implementation of a pattern recognition application or to the development of computational intelligence applications.