{"id":287,"date":"2018-06-19T14:10:28","date_gmt":"2018-06-19T18:10:28","guid":{"rendered":"http:\/\/grss-isprs.ufms.br\/?page_id=287"},"modified":"2018-07-30T13:17:45","modified_gmt":"2018-07-30T17:17:45","slug":"mini-courses-syllabus","status":"publish","type":"page","link":"https:\/\/grss-isprs.ufms.br\/en\/mini-courses-syllabus\/","title":{"rendered":"Mini-courses syllabus"},"content":{"rendered":"Mini-course 1: Machine Learning I applied to Remote Sensing Imagery<\/strong><\/p>\n

Dr. Franz Rottensteiner \u2013 Leibniz University Hannover<\/strong><\/p>\n

    \n
  1. Generative classifiers<\/li>\n
  2. Logistic Regression<\/li>\n
  3. SV and Randon Forest<\/li>\n
  4. Graphical Models (Markov and Conditional Random Fields)<\/li>\n<\/ol>\n

    Mini-course 2:\u00a0Photogrammetry applied to environmental studies<\/strong><\/p>\n

    Dr. Anette Eltner \u2013 TU Dresden<\/strong><\/p>\n

    1) Basic principles:<\/div>\n
    \u00a0 \u00a0– 3D reconstruction<\/div>\n
    \u00a0 \u00a0– Srea and feature based image matching<\/div>\n
    \u00a0 \u00a0– Structure from motion Photogrammetry<\/div>\n
    \u00a0 \u00a0–\u00a0UAV systems and flight planning<\/div>\n
    \u00a0 \u00a0– Direct and indirect geo-referencing<\/div>\n
    <\/div>\n
    2) Applications to environmental studies<\/div>\n

     <\/p>\n

    Mini-course 3: Machine Learning II applied to Remote Sensing Imagery <\/strong><\/p>\n

    Dr. Farid Melgani \u2013 University of Trento<\/strong><\/p>\n

      \n
    1. Artificial Neural Networks<\/strong><\/li>\n<\/ol>\n

      Introduction. Artificial Neuron. Multilayer Perceptron. Radial Basis Function. Probabilistic Neural Network.<\/p>\n

      Regression with Neural Networks. Deep Learning. Autoencoder. Convolutional Neural Network.<\/p>\n

        \n
      1. Support Vector Machines<\/strong><\/li>\n<\/ol>\n

        Introduction. Kernel Representation. Generalization Theory. VC Dimension. Structural Risk Minimization.<\/p>\n

        Margin-Based Bounds. Generalization for Regression. Maximal Margin Classification. Soft Margin<\/p>\n

        Classification. Multiclass Support Vector Machines. Support Vector Regression.","protected":false},"excerpt":{"rendered":"

        Mini-course 1: Machine Learning I applied to Remote Sensing Imagery Dr. Franz Rottensteiner \u2013 Leibniz University Hannover Generative classifiers Logistic Regression SV and Randon Forest Graphical Models (Markov and Conditional Random Fields) Mini-course 2:\u00a0Photogrammetry applied to environmental studies Dr. Anette Eltner \u2013 TU Dresden 1) Basic principles: \u00a0 \u00a0– 3D reconstruction \u00a0 \u00a0– Srea and […]<\/p>\n","protected":false},"author":6465,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"ngg_post_thumbnail":0,"footnotes":""},"coauthors":[],"class_list":["post-287","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/grss-isprs.ufms.br\/en\/wp-json\/wp\/v2\/pages\/287","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/grss-isprs.ufms.br\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/grss-isprs.ufms.br\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/grss-isprs.ufms.br\/en\/wp-json\/wp\/v2\/users\/6465"}],"replies":[{"embeddable":true,"href":"https:\/\/grss-isprs.ufms.br\/en\/wp-json\/wp\/v2\/comments?post=287"}],"version-history":[{"count":7,"href":"https:\/\/grss-isprs.ufms.br\/en\/wp-json\/wp\/v2\/pages\/287\/revisions"}],"predecessor-version":[{"id":443,"href":"https:\/\/grss-isprs.ufms.br\/en\/wp-json\/wp\/v2\/pages\/287\/revisions\/443"}],"wp:attachment":[{"href":"https:\/\/grss-isprs.ufms.br\/en\/wp-json\/wp\/v2\/media?parent=287"}],"wp:term":[{"taxonomy":"author","embeddable":true,"href":"https:\/\/grss-isprs.ufms.br\/en\/wp-json\/wp\/v2\/coauthors?post=287"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}