{"id":85010,"date":"2019-04-24T15:38:26","date_gmt":"2019-04-24T13:38:26","guid":{"rendered":"https:\/\/uniavisen.dk\/event\/diku-bits-transfer-learning-to-estimate-influenza-prevalence\/"},"modified":"2019-04-24T15:38:26","modified_gmt":"2019-04-24T13:38:26","slug":"diku-bits-transfer-learning-to-estimate-influenza-prevalence","status":"publish","type":"event","link":"https:\/\/uniavisen.dk\/en\/event\/diku-bits-transfer-learning-to-estimate-influenza-prevalence\/","title":{"rendered":"DIKU Bits: Transfer learning to estimate influenza prevalence"},"content":{"rendered":"

Speaker<\/h3>\n

Ingemar J. Cox, professor in the Machine Learning Section at DIKU.<\/p>\n

Abstract<\/h3>\n

It is now well-known that web searches can be\u00a0used to estimate the prevalence of influenza (and other diseases) in a\u00a0population. It is often claimed that\u00a0this approach is especially useful in low\u00a0and middle income countries (LMIC) where traditional health surveillance\u00a0infrastructures are poor or absent.\u00a0However, almost all proposed methods require\u00a0training data that is often unavailable in LMIC regions. To address this issue,\u00a0we describe a transfer\u00a0learning solution where we train a model in say the USA,\u00a0and then transfer the model to say\u00a0Zimbabwe.<\/p>\n

Zooming in on Ingemar Cox<\/h2>\n

Which courses do you teach at DIKU (BSc and MSc)?<\/h4>\n

Information retrieval, sometimes Web Science<\/p>\n

Which technology\/research\/projects\/start-ups are you exited to see develop?<\/h4>\n

AI for health<\/p>\n

What is your favorite sketch from DIKUrevyen?<\/h4>\n

I don\u2019t know what this is.<\/p>\n","protected":false},"excerpt":{"rendered":"

Speaker Ingemar J. Cox, professor in the Machine Learning Section at DIKU. Abstract It is now well-known that web searches can be\u00a0used to estimate the prevalence of influenza (and other diseases) in a\u00a0population. It is often claimed that\u00a0this approach is especially useful in low\u00a0and middle income countries (LMIC) where traditional health surveillance\u00a0infrastructures are poor or […]<\/p>\n","protected":false},"author":0,"featured_media":85011,"template":"","class_list":["post-85010","event","type-event","status-publish","has-post-thumbnail","hentry","event_category-seminar"],"acf":[],"aioseo_notices":[],"yoast_head":"\nDIKU Bits: Transfer learning to estimate influenza prevalence<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/uniavisen.dk\/en\/event\/diku-bits-transfer-learning-to-estimate-influenza-prevalence\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"DIKU Bits: Transfer learning to estimate influenza prevalence\" \/>\n<meta property=\"og:description\" content=\"Speaker Ingemar J. Cox, professor in the Machine Learning Section at DIKU. Abstract It is now well-known that web searches can be\u00a0used to estimate the prevalence of influenza (and other diseases) in a\u00a0population. 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