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2022<\/strong><\/p>\n <\/p>\n Shen Liang [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”3.0.47″][et_pb_text module_id=”contenu” _builder_version=”3.22.1″ box_shadow_style=”preset3″ custom_padding=”15px|15px|15px|15px|true|true” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/shen-liang-deep-transfer-learning-and-multi-task-learning\/” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n 2022<\/strong><\/p>\n <\/p>\n Shen Liang [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”3.0.47″][et_pb_text module_id=”contenu” _builder_version=”3.22.1″ box_shadow_style=”preset3″ custom_padding=”15px|15px|15px|15px|true|true” z_index_tablet=”500″ 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transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n 2021<\/strong><\/p>\n <\/p>\n Foula Vagena [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row make_equal=”on” _builder_version=”3.22.1″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”][et_pb_column type=”1_3″ _builder_version=”3.0.47″][et_pb_text module_id=”contenu” _builder_version=”3.22.1″ box_shadow_style=”preset3″ custom_padding=”15px|15px|15px|15px|true|true” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/foula-vagena-data-science-a-high-level-overview\/” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n 2021<\/strong><\/p>\n <\/p>\n Foula Vagena [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”3.0.47″][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”3.0.47″][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”3.22.1″ custom_margin=”200px||”][et_pb_row _builder_version=”3.22.1″][et_pb_column type=”4_4″ _builder_version=”3.22.1″][et_pb_divider admin_label=”Divider” _builder_version=”3.22.1″][\/et_pb_divider][et_pb_text admin_label=”\u00c0 lire aussi” _builder_version=”3.22.1″ locked=”off”]<\/p>\n [\/et_pb_text][et_pb_blog posts_number=”4″ include_categories=”13″ show_author=”off” show_date=”off” show_pagination=”off” module_id=”page_type_blog” _builder_version=”3.21″ header_level=”h4″ border_width_bottom_fullwidth=”1px” border_color_bottom_fullwidth=”rgba(51,51,51,0.18)” custom_padding=”||50px|” locked=”off”][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" 2022 Shen LiangDeep Domain Adaptation and Generalization2022 Shen LiangDeep Transfer Learning and Multi-task Learning2022 Shen LiangKnowledge-guided Data Science2022 Foula VagenaGenerative Models + Hands-On Workshop 2022 Foula VagenaGenerative Models\u00a02022 Foula VagenaStatistical Machine Learning: Interence and Learning\u00a02022 Foula VagenaStatistical Machine Learning: Overview and Applications2022 Foula VagenaGraph Based Data Science:… Lire la suite Seminars + Hands-On Workshops<\/span><\/a><\/p>\n","protected":false},"author":560,"featured_media":0,"parent":1715,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"class_list":["post-2839","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/pages\/2839","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/users\/560"}],"replies":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/comments?post=2839"}],"version-history":[{"count":12,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/pages\/2839\/revisions"}],"predecessor-version":[{"id":3164,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/pages\/2839\/revisions\/3164"}],"up":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/pages\/1715"}],"wp:attachment":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media?parent=2839"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Deep Domain Adaptation and Generalization<\/span><\/strong><\/p>\n
Deep Transfer Learning and Multi-task Learning<\/span><\/strong><\/p>\n
Knowledge-guided Data Science<\/span><\/strong><\/p>\n
Generative Models + Hands-On Workshop<\/span><\/strong><\/p>\n
Generative Models\u00a0<\/span><\/strong><\/p>\n
Statistical Machine Learning: Interence and Learning\u00a0<\/span><\/strong><\/p>\n
Statistical Machine Learning: Overview and Applications<\/span><\/strong><\/p>\n
Graph Based Data Science: Opportunities Challenges and Techniques<\/span><\/strong><\/p>\n
Ensemble Learning: Theory and Techniques\u00a0<\/span><\/strong><\/p>\n
Deep Learning for Sequential Data: Models and Applications<\/span><\/strong><\/p>\n
Convolutional Neural Networks: An Overview and Applications<\/span><\/strong><\/p>\n
Deep Learning: An overview using Multi Layer Perceptrons (MLPs)<\/span><\/strong><\/p>\n
Data Science: A high level overview<\/span><\/strong><\/p>\nBy the way<\/h2>\n