{"id":138,"date":"2019-01-11T13:06:30","date_gmt":"2019-01-11T16:06:30","guid":{"rendered":"https:\/\/eventos.cmm.uchile.cl\/ipmas2023\/?page_id=138"},"modified":"2023-09-29T09:12:03","modified_gmt":"2023-09-29T12:12:03","slug":"short-courses","status":"publish","type":"page","link":"https:\/\/eventos.cmm.uchile.cl\/ipmas2023\/short-courses\/","title":{"rendered":"Short Courses"},"content":{"rendered":"
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To register for one or more courses please fill the form<\/strong> at the end of this page.<\/p>\n

[su_button url=”https:\/\/eventos.cmm.uchile.cl\/ipmas2023\/venue” target=”self” style=”flat” background=”#2B3081″ color=”#FFFFFF” size=”5″ wide=”no” center=”no” radius=”auto” icon=”icon: location-arrow” icon_color=”#FFFFFF” text_shadow=”none”] Sala de los cursos [\/su_button]<\/p>\n

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Note: all the courses will be in Spanish<\/p>\n

Course 1: An Introduction to physics-informed machine learning<\/h3>\n

In this course we will cover the basics of machine learning and physical modeling. In particular, we will learn about the terminology in these areas and their connections, and how to combine machine learning with physical models to get the better of both worlds. The course will culminate with practical applications of these tools in a workshop.<\/span><\/p>\n

Date: <\/strong>to be defined (number of modules)<\/p>\n

\u00a0<\/strong>Instructor:<\/strong> Francisco Sahli (PUC,Chile)<\/p>\n

Abstract:<\/strong><\/p>\n

In this course we will cover the basics of machine learning and deep learning. In particular, we will learn about the busy terminology in these areas and its connections with statistical concepts, in addition to learning what neural networks are and how they are trained. The course will culminate with practical applications of these tools in a workshop.<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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Course 2: Introduction to inverse problems for data analysis<\/h3>\n

Date:<\/strong> to be defined (number of modules)<\/p>\n

Instructor:<\/strong> Claudia Prieto (PUC), Axel Osses (U. de Chile)<\/p>\n

Abstract:<\/strong><\/p>\n<\/div>\n<\/div>\n<\/div>\n

In this course some basic concepts of inverse problems will be reviewed from the point of view of parameter identification, data assimilation and regularization. The theoretical classes will be accompanied by practical laboratories with applications related to the management of images through jupyter notebooks.<\/p>\n<\/div>\n

For the practical sessions you will need:<\/strong><\/p>\n