[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_1643615805343{margin-left: -15px !important;padding-top: 5px !important;}”] Syllabus
Moodle
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Learning outcomes:
Students should be able:
– to provide the analysis of large data arrays, based on the information and data logical models, by using neural network technologies to solve data processing problems in subject areas.
– 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 lassification andforecasting problems.
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