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Abstract

With the modern availability of data and computing resources, machine learning is becoming an important tool in solving problems in many domains, from robotics and medicine to weather and climate. In this talk I will attempt to give an accessible introduction to core machine learning concepts, aimed at beginners in the topic. I’ll walk through ideas like supervised, unsupervised and representation learning and share some examples from my own research. I will end by looking at recent developments in large general-purpose foundation models, such as AURORA, and how they’re being applied in climate forecasting and beyond.

Speaker information

Linus Ericsson is a postdoctoral researcher at the University of Edinburgh, specialising in representation learning, neural architectures, and domain adaptation. His current research explores efficient ways of finding the right neural network structure for the right task and how to make existing networks cheaper to run. Linus has published in top venues such as NeurIPS, CVPR, and the IEEE Signal Processing Magazine. His interests span a wide array of topics, including multimodal learning and responsible AI applications in climate and healthcare.