Intelligent Systems for pattern recognition
Code 651AA
Credits 6
Learning outcomes
The course introduces students to the design of A.I. based solutions to complex pattern recognition problems and discusses how to realize applications exploiting computational intelligence 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 time-series 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
• Neural network models for pattern recognition on non-vectorial data (physiological data, sensor streams, etc)
• Kernel and adaptive methods for relational data
• Pattern recognition applications: machine vision, bio-informatics, robotics, medical imaging, etc.
• ML and deep learning libraries overview: e.g. scikit-learn, Keras, Theano
A final project will introduce students to the implementation of a pattern recognition application or to the development of computational intelligence applications.
• Signal processing and time-series analysis
• Image processing, filters and visual feature detectors
• Bayesian learning and deep learning for machine vision and signal processing
• Neural network models for pattern recognition on non-vectorial data (physiological data, sensor streams, etc)
• Kernel and adaptive methods for relational data
• Pattern recognition applications: machine vision, bio-informatics, robotics, medical imaging, etc.
• ML and deep learning libraries overview: e.g. scikit-learn, Keras, Theano
A final project will introduce students to the implementation of a pattern recognition application or to the development of computational intelligence applications.