{"id":22,"date":"2014-09-25T09:50:40","date_gmt":"2014-09-25T09:50:40","guid":{"rendered":"http:\/\/iict-space.heig-vd.ch\/ape\/?page_id=22"},"modified":"2022-12-08T10:14:01","modified_gmt":"2022-12-08T10:14:01","slug":"teaching","status":"publish","type":"page","link":"http:\/\/iict-space.heig-vd.ch\/ape\/teaching\/","title":{"rendered":"Teaching"},"content":{"rendered":"\n<p><strong>Bachelor Courses<\/strong><\/p>\n\n\n\n<ul><li><strong>Introduction \u00e0 la science des donn\u00e9es (ISD, 2021)<\/strong> <ul><li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/Uo5DspUkttD6QZL\">1. Outils pour la mod\u00e9lisation data-driven<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/miJC9LAfthDEr8V\">2. Introduction \u00e0 la science des donn\u00e9es<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/FNDHWVRsHuxQqrD\">3. Apprentissage automatique (Machine Learning)<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/jPH4qywib3ZDmqu\">4. Biblioth\u00e8ques pour le calcul scientifique<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/rI0aCethKFAHgQK\">5. Analyse exploratoire des donn\u00e9es<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/GB8hTHNXb46vddU\">6. Apprentissage supervis\u00e9<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/sghUAZpEg1cn9Jy\">7. Evaluation des mod\u00e8les<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/b192geUuGP8n4OA\">8. Apprentissage supervis\u00e9 2: algorithme LVQ<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/wc91QcCzylBEyNo\">9. R\u00e9gression lin\u00e9aire<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/mO3zH3GlC0M55rK\">10. Donn\u00e9es et caract\u00e9ristiques<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/6854gWaKMZquSGp\">11. Apprentissage non-supervis\u00e9<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/CrOiu2jJTW1r1PK\">12. Conclusions et perspectives<\/a>  <\/li>\n<\/ul>\n<\/ul>\n<ul>\n<li><strong>Apprentissage par r\u00e9seaux de neurones (ARN, 2022)<\/strong> <ul><li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/kSZht2MorBjf92f\">1. Machine Learning basics (reminder)<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/txVpqJxC5orajn9\">2. Perceptrons<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/tczLJVkQH3B9MLS\">3. Multi-Layer Perceptrons (MLP)<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/l5GvjKVS1G0Fbnr\">4. Neural Networks&#8217; training<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/xHpotI8EjVE5Txz\">5. Neural Networks&#8217; monitoring<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/MXL5F5Wx8h3HmtR\">6. Convolutional Neural Networks (CNN)<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/HaoIb7FSGI5BMps\">7. From Shallow to Deep Neural Networks<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/DU6tAU63gVYlBs0\">8. Convolutional Neural Network Architectures<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/2fU68bILX2BxmKy\">9. Transfer Learning, Embeddings and Meta-Learning<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/ow7XNK2hMDHygEO\">10. Deep troubles<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/DnuD6AfOw1utm0x\">11. Deep Learning Applications<\/a><\/li>\n<li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/hVb2jcKcU49z8jS\">12. Beyond Convolutional Neural Networks<\/a><\/li>\n<\/ul><\/ul>\n<ul><li><strong>Machine Intelligence (MIN 2023-)<\/strong> <ul><li>01. Introduction: from GOFAI to modern AI <\/li><li>02. Self-supervised learning <\/li><li>03. Generative Adversarial Networks <\/li><li>04. Machine Learning and creativity <\/li><li>05. Reinforcement Learning <\/li><li>06. Deep Reinforcement Learning <\/li><li>07. Reinforcement Learning applications<\/li><li>08. Artificial Evolution <\/li><li>09. Embodied Cognition <\/li><li>10. From Collective Intelligence to Machivellian Intelligence <\/li><li>11. Agent-based models, Artificial Life and Complexity <\/li><li>12. Agent-based model applications  <\/li><\/ul><\/ul>\n\n<ul><li><strong>Machine Learning (MLG 2016-2020)<\/strong> <ul><li>01. <a href=\"https:\/\/drive.switch.ch\/index.php\/s\/Y7Ye68aw5zORUwO\">Introduction<\/a><\/li><li>02. <a href=\"https:\/\/drive.switch.ch\/index.php\/s\/oPFFE4ANw9f0gEQ\">Artificial Neural Networks<\/a><\/li><li>03. <a href=\"https:\/\/drive.switch.ch\/index.php\/s\/cV5W4vkp8qYOjAj\">Artificial Neural Networks II<\/a><\/li><li>04. <a href=\"https:\/\/drive.switch.ch\/index.php\/s\/lWUlCfNNqTOrO7M\">Feature engineering<\/a><\/li><li>05. <a href=\"https:\/\/drive.switch.ch\/index.php\/s\/VUiA2JD5DDKak8E\">Deep Learning<\/a><\/li><li>06. <a href=\"https:\/\/drive.switch.ch\/index.php\/s\/q0bwEVS3GyLyf14\">Unsupervised learning<\/a> <\/li><\/ul><ul><li><span style=\"font-size: 15px\">07.&nbsp;<a href=\"https:\/\/drive.switch.ch\/index.php\/s\/Vmex8Cti6jD2vyf\">Reinforcement Learning<\/a><\/span><\/li><li><span style=\"font-size: 15px\">08. <a href=\"https:\/\/drive.switch.ch\/index.php\/s\/ZtwT1nnQySNdn1t\">Genetic Algorithms<\/a><\/span><\/li><li>09. <a href=\"https:\/\/drive.switch.ch\/index.php\/s\/DmgEY77v8yQxD2W\">Swarm Intelligence<\/a><\/li><li>10. <a href=\"https:\/\/drive.switch.ch\/index.php\/s\/NwjP3WNh3EWsXqW\">Artificial Intelligence<\/a><\/li><\/ul><\/li><\/ul><ul><li><strong>Interfaces Homme-Machine (IHM, 2018-2022)<\/strong> <ul><li>0. <a href=\"https:\/\/drive.switch.ch\/index.php\/s\/Xq4pQ7xlJkIGeZk\">Introduction<\/a><\/li><li>1. <a href=\"https:\/\/drive.switch.ch\/index.php\/s\/JekclHyEpIcTFuC\">Design of interfaces: basic concepts<\/a><\/li><li>2. <a href=\"https:\/\/drive.switch.ch\/index.php\/s\/tg695Z5oTue79FS\">The human in the loop<\/a><\/li><li>3. <a href=\"https:\/\/drive.switch.ch\/index.php\/s\/5TNAiZi1fRJpoTZ\">Context-aware interfaces<\/a><\/li><li>4. <a href=\"https:\/\/drive.switch.ch\/index.php\/s\/JmPWuaz2j2V4Pnk\">Emerging HCI<\/a><\/li><li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/bxn73qdQiF0VPHr\">5.&nbsp;<span style=\"font-size: 15px\">Data visualization<\/span><\/a><\/li><li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/VGX9YDrXO2rl3Lo\">6. Gamification<\/a> <\/li><\/ul><\/li><\/ul><ul><li><strong>Architecture des ordinateurs (ARO1, 2003-2018)<\/strong><\/li><li><strong>D\u00e9veloppement de dispositifs m\u00e9dicaux (DDM, 2019 &amp; 2020)<\/strong> <ul><li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/0t3bH8wSGOjCT6G\">Sports, sant\u00e9 et bien-\u00eatre \u00e0 l&#8217;\u00e8re du num\u00e9rique<\/a><\/li><li><a href=\"https:\/\/drive.switch.ch\/index.php\/s\/TEiSen3JHnveCQY\">Sports, sant\u00e9 et bien-\u00eatre \u00e0 l&#8217;\u00e8re du num\u00e9rique 2<\/a> <\/li><\/ul>\n\n<\/ul><li><strong>MAGICIEL:MAt\u00e9riel et LoGICIEL des ordinateurs (MAG, 2004-2014)<\/strong> <\/li>\n<li><strong>Science-fiction et technologie (SFI, 2015)<\/strong> <\/li><li><strong>Syst\u00e8mes bio-inspir\u00e9s (SBI, 2005-2015)<\/strong><\/li>\n<\/ul><\/ul>\n<ul><\/ul>\n\n\n\n<p class=\"has-text-align-left\"><strong>Master&nbsp;Courses<\/strong><\/p>\n\n\n\n<ul><li><strong>Machine Learning (TSM_MachLe, 2017-)<\/strong> <ul><li>9. Artificial Neural Networks<\/li><li>10. Deep Learning &amp;\u00a0Convolutional Neural Networks<\/li><li>12. Autoencoders<\/li><li>13. Recurrent Neural Networks<\/li><li>14. Dimensionality reduction <\/li><\/ul><\/li><li><strong>Machine Learning on Big Data (MLBD 2016-)<\/strong> <ul><li>1. Introduction<\/li><li>2. Image processing using Convolutional Neural Networks<\/li><li>3. Remote Sensing: a Big Data case study<\/li><li>4. Change and Anomaly detection in Big Spatiotemporal Data. <\/ul>\n\n\n\n<li><strong>Quantified Self (QSelf, 2018-2021)<\/strong> <ul><li>1. Introduction to Quantified Self<\/li><li>2. Sensors for Quantified Self<\/li><li>3. Sensor data for Quantified Self<\/li><li>4. Quantified Self: physical state monitoring<\/li><li>5. Quantified Self: cognitive state monitoring <\/li><\/ul><\/li><li><strong>AI for Games and Simulation (AIGS, 2012 &amp; 2013)<\/strong> <\/li><li><strong>Smart Devices and Applications (SDA, 2010 &amp; 2011)<\/strong><\/li><\/ul>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Bachelor Courses Introduction \u00e0 la science des donn\u00e9es (ISD, 2021) 1. Outils pour la mod\u00e9lisation data-driven 2. Introduction \u00e0 la science des donn\u00e9es 3. Apprentissage automatique (Machine Learning) 4. Biblioth\u00e8ques pour le calcul scientifique 5. Analyse exploratoire des donn\u00e9es 6. <a class=\"more-link\" href=\"http:\/\/iict-space.heig-vd.ch\/ape\/teaching\/\">Continue reading <span class=\"screen-reader-text\">  Teaching<\/span><span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":10,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","meta":[],"_links":{"self":[{"href":"http:\/\/iict-space.heig-vd.ch\/ape\/wp-json\/wp\/v2\/pages\/22"}],"collection":[{"href":"http:\/\/iict-space.heig-vd.ch\/ape\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/iict-space.heig-vd.ch\/ape\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/iict-space.heig-vd.ch\/ape\/wp-json\/wp\/v2\/users\/10"}],"replies":[{"embeddable":true,"href":"http:\/\/iict-space.heig-vd.ch\/ape\/wp-json\/wp\/v2\/comments?post=22"}],"version-history":[{"count":226,"href":"http:\/\/iict-space.heig-vd.ch\/ape\/wp-json\/wp\/v2\/pages\/22\/revisions"}],"predecessor-version":[{"id":672,"href":"http:\/\/iict-space.heig-vd.ch\/ape\/wp-json\/wp\/v2\/pages\/22\/revisions\/672"}],"wp:attachment":[{"href":"http:\/\/iict-space.heig-vd.ch\/ape\/wp-json\/wp\/v2\/media?parent=22"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}