{"id":11638,"date":"2022-11-09T12:20:05","date_gmt":"2022-11-09T11:20:05","guid":{"rendered":"https:\/\/www.ifabfoundation.org\/?p=11638"},"modified":"2022-11-29T11:08:18","modified_gmt":"2022-11-29T10:08:18","slug":"machine-learning-shaping-the-future-of-weather-and-climate-ifab-and-ecmwf-launch-the-massive-open-online-course-mooc","status":"publish","type":"post","link":"https:\/\/www.ifabfoundation.org\/en\/2022\/11\/09\/machine-learning-shaping-the-future-of-weather-and-climate-ifab-and-ecmwf-launch-the-massive-open-online-course-mooc\/","title":{"rendered":"Machine Learning shaping the future of Weather and Climate: IFAB and ECMWF launch the \u201cMassive Open Online Course (MOOC)\u201d"},"content":{"rendered":"<p>Current times are strongly affected by <strong>climate change<\/strong> and by future scenarios:<strong> extreme weather events<\/strong> are always more frequent and more relevant, all over our planet. In contexts of increasing emergency,<strong> technology<\/strong> represents the most important tool for designing<strong> strategic solutions<\/strong>, suitable for managing these phenomena. <strong>Machine learning<\/strong> is a fast-growing field of study, on which the new programme promoted by <a href=\"https:\/\/www.ecmwf.int\/\" target=\"_blank\" rel=\"noopener\">ECMWF<\/a> and IFAB is focused.<\/p>\n<p>The course is called \u201c<strong>Massive Open Online Course (MOOC) on Machine Learning in Weather and Climate<\/strong>\u201d and wants to train a community of researchers and experts specialized on the potential and opportunities of Machine Learning in the fields of weather and weather forecasts.<\/p>\n<p>Here below the information on the programme.<\/p>\n<h4>\u25ba Who is the MOOC for?<\/h4>\n<p><strong>The enrollment is open to anyone interested in the investigated topics<\/strong> &#8211; academics, professionals of operational services, policymakers and administrators, general public. A basic knowledge of climate science, statistics and computing is required. At the end of the programme, learners will have the opportunity to take part in the ECMWF\u2019s Summer of Weather Code (ESoWC) with their own coding projects. More information <a href=\"https:\/\/esowc.ecmwf.int\" target=\"_blank\" rel=\"noopener\">here<\/a>.<\/p>\n<h4>\u25ba How is the course structured?<\/h4>\n<p>The MOOC is structured on <strong>three different tiers<\/strong>. Each student shall decide to follow one or more tiers.<\/p>\n<ul>\n<li>First tier: introduction to Machine Learning in weather and climate<\/li>\n<li>Second tier: Focus on Machine Learning opportunities<\/li>\n<li>Third tier: practical machine learning applications in weather and climate<\/li>\n<\/ul>\n<p>Whilst the first tier represents a theoretical focus on Machine Learning, the last two tiers follow a more hands-on approach and include coding assignments. Therefore, they are more suitable for learners who deeply know and use Big Data, such as academics or members of the private sector (industry, production, etc.) Moreover, within each tier the programme is modular: participants may choose one or more modules according to their interests.<\/p>\n<h4>\u25ba How long does it last?<\/h4>\n<p>The MOOC runs over <strong>10 weeks<\/strong>, with around three or four hours of study per week. In total, <strong>36 hours of training<\/strong>.<\/p>\n<h4>\u25ba What are the training methods?<\/h4>\n<p>The MOOC is an online training programme with a <strong>multi-disciplinary approach<\/strong>. Learning will be consolidated through a wide range of <strong>e-learning activities<\/strong>, from <strong>videos<\/strong> to <strong>podcasts<\/strong>, from in-depth modules to <strong>webinars<\/strong>, during which participants will be able to interact with leading experts from the field. The acquired skills will be then honed with practical exercises. Using interactive Jupiter notebooks, learners will be able to gain hands-on experience with Machine Learning algorithms. Learners may also count on the collaboration of Lisa Burke, dedicated science communicator, who will break down technical content into plain language.<\/p>\n<h4>\u25ba How to enroll?<\/h4>\n<p><strong>Registration is free<\/strong> and open to <a href=\"https:\/\/lms.ecmwf.int\/pages\/index.html\" target=\"_blank\" rel=\"noopener\">this link<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Current times are strongly affected by climate change and by future scenarios: extreme weather events are always more frequent and more relevant, all over our planet. In contexts of increasing emergency, technology represents the most important tool for designing strategic solutions, suitable for managing these phenomena. Machine learning is a fast-growing field of study, on&#8230;<\/p>\n","protected":false},"author":1,"featured_media":11644,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[99,28],"tags":[],"class_list":["post-11638","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-courses","category-news"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Machine Learning shaping the future of Weather and Climate: IFAB and ECMWF launch the \u201cMassive Open Online Course (MOOC)\u201d - IFAB International Foundation<\/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:\/\/www.ifabfoundation.org\/en\/2022\/11\/09\/machine-learning-shaping-the-future-of-weather-and-climate-ifab-and-ecmwf-launch-the-massive-open-online-course-mooc\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning shaping the future of Weather and Climate: IFAB and ECMWF launch the \u201cMassive Open Online Course (MOOC)\u201d - IFAB International Foundation\" \/>\n<meta property=\"og:description\" content=\"Current times are strongly affected by climate change and by future scenarios: extreme weather events are always more frequent and more relevant, all over our planet. 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