[vc_section][vc_row][vc_column][vc_row_inner][vc_column_inner width=”1/4″][vc_single_image image=”2733″ img_size=”full” el_class=”.non-padding” css=”.vc_custom_1572442136367{margin-right: -15px !important;margin-left: -15px !important;}”][/vc_column_inner][vc_column_inner width=”1/2″][/vc_column_inner][vc_column_inner width=”1/4″][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column width=”1/4″][vc_column_text css=”.vc_custom_1643615761686{margin-left: -15px !important;padding-top: 5px !important;}”] Syllabus  

 

Moodle  

 

Learning materials   [/vc_column_text][/vc_column][vc_column width=”3/4″][vc_column_text]

Learning outcomes:

Students should be able:
– to provide extraction of models from data and support of engineering activities, including through data processing, by applying methods and algorithms of deep learning, including using machine learning methods to solve classification and forecasting problems.

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