Announcing a new eLearning series available now on Unidata eLearning: Supervised Machine Learning Readiness. This learning series is a self-paced, beginner-friendly program designed for Earth systems scientists to explore the core principles of supervised machine learning. This series uses a combination of step-by-step frameworks, exploratory widgets, and low-code exercises in Jupyter Notebooks, to explore the full cycle of machine learning model development. No programming experience is required. By the end of the series, you will be able to recognize when machine learning is an appropriate tool and critically evaluate machine learning in Earth systems science contexts.
If you provide Earth Systems Science learning opportunities at the post-secondary levels or within the workforce, UCAR needs your input! UCAR would love to hear about any learning opportunities that you offer on emerging ESS capabilities — ranging from AI/ML and data management to relationship building, creativity, and more — that are needed in the workplace now and in the future. Help UCAR understand your priorities and any obstacles to providing education, training, and support for these capabilities.
The Emerging Pedagogies Summit is an annual event hosted by the Learning Innovation and Lifetime Education (LILE) group at Duke University, and I, Nicole Corbin, instructional designer at NSF Unidata, had the pleasure of attending. This year's event was packed with thoughtfully curated topics relevant to the NSF Unidata higher education community, including AI and workforce development.
NSF Unidata is at the 2024 Earth Educators' Rendezvous in Philadelphia, PA the week of July 15-19, 2024. Join Instructional Designer Nicole Corbin and AI/ML Software Engineer Thomas Martin on Friday's poster session to discuss their ongoing collaboration with Metropolitan State University of Denver, Machine Learning Foundations and Applications in the Earth Systems Sciences.
Announcing a new eLearning module available now on Unidata eLearning: Machine Learning Foundations in the Earth Systems Sciences. This no-code module is designed to guide you through the very basics of supervised machine learning in the Earth Systems Sciences. You will discover how machine learning is currently being used by scientists, examine the process for supervised machine learning model development, explore how data plays a crucial role in making good predictions, and how to be an effective and ethical user of machine learning tools. You will also learn that machine learning is not a catch-all solution to every problem!
Announcing two new microlearning resources available now on Unidata eLearning. Microlearning is a modular approach to online learning that focuses on a single objective. These resources can be appended to existing activities for just-in-time foundational scaffolding in a quick five to ten minutes. The duo of modules releasing today focus on foundational data literacy within the Earth Systems Sciences.
The World Meteorological Organization (WMO) WMO Global Campus is the collaborative network of WMO Member institutions and National Meteorological Hydrological Services involved in the development and delivery of education and training. Its goal is to address the evolving global priorities for learning. It is the fruit of the synergies, sharing and cooperation within this community of institutions. In keeping with the concept of a WMO Global Campus as a community of practice, WMO is initiating the publication of a volume of short papers describing case studies of innovations implemented by partners in the Education and Training Programme (ETRP).
