{"id":46,"date":"2018-01-08T15:30:44","date_gmt":"2018-01-08T15:30:44","guid":{"rendered":"https:\/\/edu.eledia.org\/courses\/esoa-2018-madonna-di-campiglio\/?page_id=46"},"modified":"2024-10-01T12:00:54","modified_gmt":"2024-10-01T11:00:54","slug":"front-page","status":"publish","type":"page","link":"https:\/\/edu.eledia.org\/courses\/phd-school-2022-machine-learning\/","title":{"rendered":"Front Page"},"content":{"rendered":"

COURSE PURPOSES<\/h3>

\nUnderstanding and solving complex problems in the physical world has been an intelligent endeavor of humankind. Moreover, the study of artificial intelligence (AI) embodies the dream of designing machines like humans. Research in machine learning (ML) and, more recently, on deep learning (DL) techniques has attracted much attention in many engineering fields. With the spreading of such techniques, improvement in learning capacity may allow machines to \u201clearn\u201d from a large amount of physical data and \u201cmaster\u201d the physical laws in certain controlled boundary conditions. In the long run, a hybridization of fundamental physical principles with \u201dknowledge\u201d from big data could unleash numerous engineering applications that used to be impossible due to the limit of data information and computational capabilities.\n<\/p>\n

\nThe course aims at providing a solid background knowledge on AI and ML, with a focus on recent and competitive methodologies for the efficient and robust solution of both classification and regression problems in advanced engineering applications. Applicative examples including exercises will corroborate the theoretical concepts.\n<\/b><\/p>\n
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\n\tCourse Topics<\/b>\n\t