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Welcome to the SciML Leeds Hackathon! This exciting event will focus on using machine learning to identify the dominant ice-water interface via a fusion of SAR and optical imagery. This hackathon is designed to be accessible to participants of all disciplines and skill levels. Whether you're a machine learning enthusiast or simply curious about its...
Data-driven models, especially machine learning-based models such as recurrent neural networks, are a popular choice for time-series forecasting because they can capture spatiotemporal structures from timeseries data, without reference to the mechanics governing the underlying phenomenon. However, their ability to generalize robustly depends on how well-represented the governing dynamics are in the data. This is...
The Weights and Biases workshop is a hands-on event designed for researchers seeking to stay up-to-date with the latest developments in machine learning. The workshop will be led by an experienced ML engineer who will guide participants through practical applications of Weights and Biases in their ML projects. Attendees will have the opportunity to learn...
This event is primarily an in-person event, however an online option is available to attend the talk in the first hour (3-4pm). REGISTER NOW Speaker: Alhanof Alolyan, Ph.D. candidate at School of Computing Reinforcement learning (RL) might serve as an investigative way to explore unexpected and valuable findings in your work. Have you ever considered...
Friday 17th February 2023 2-4pm In person only event Room 11.87, Leeds Institute for Data Analytics (LIDA), Level 11, Worsley Building Speaker: Fergus Shone, PhD Researcher, University of Leeds Do you have sparse, low-quality data, but a good understanding of the physical system you are modelling? Then physics-informed neural networks (PINNs) might be the machine learning...
Friday 10th February 2023 2-3pm Hybrid / Room 11.87 Speaker: Björn Lütjens, PhD candidate (MIT) REGISTER NOW Decision-makers in industry are grappling with climate change and ask for local climate risk analyses. Local analyses, however, are largely inaccessible, because running Earth system models at the local scale suffers from the curse of dimensionality and climate datasets...
Friday 13th January - 2-3pm - Online Speaker - Zack Labe, Princeton REGISTER TODAY The popularity of machine learning methods, such as neural networks, is rapidly expanding in nearly all areas of Earth science. The interest in these tools also coincides with a growing influx of big data and the need for high efficiency in...