|Teoria||5||II semestre||Marco Cristani|
|Laboratorio||1||II semestre||Marco Cristani|
The course aims to provide the theoretical foundations and describe the main methodologies relating to the machine learning area. In particular, the course will deal with the methods of analysis, recognition and automatic classification of data of any type, typically called patterns. These disciplines are the basis, are used, and often complete many other disciplines and application areas of wide diffusion, such as computational vision, robotics, image processing, data mining, analysis and interpretation of medical and biological data, bioinformatics, biometrics, video surveillance, speech and text recognition and many others. More precisely, the methodologies that will be introduced in the course are often an integral part of the aforementioned application areas, and constitute the "intelligent" part with the final objective of understanding (classifying, recognizing, analyzing) the data coming from the process of interest ( whether they are signals, images, strings, categorical, or other types). Starting from the type of measured data, the entire analysis pipeline will be considered such as the extraction and selection of characteristics; supervised and unsupervised machine learning methods, parametric and non-parametric analysis techniques, and validation protocols. Finally, the recent deep learning techniques will be analyzed in general with some case studies. In conclusion, the course aims to provide the student with a set of theoretical foundations and algorithmic tools to address the problems that can be encountered in strategic and innovative industrial sectors such as those involving the processing of large amounts of data (big data), multimedia, visual inspection of products and automation in general.
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