[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_1641483005038{margin-left: -15px !important;padding-top: 5px !important;}”] Syllabus
Moodle
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Learning outcomes:
Overview of biomedical engineering and the challenges in this field, artificial intelligence and deep learning, introduction to classification- linear regression, kNN, optimization- basic principles , cost functions and optimization methods, neural networks, convolutional neural networks, training neural networks, deep learning software, CNN architectures, recurrent neural networks, detection and segmentation, data augmentation and generation, selected applications: fMRI, mammography, ECG, speech signal processing, additional examples.
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