First Fall School on AutoML – invited talk
AutoML for healthcare
AutoML and interpretability are both fundamental to the successful uptake of machine learning by non-expert end users. The former will lower barriers to entry and unlock potent new capabilities that are out of reach when working with ad-hoc models, while the latter will ensure that outputs are transparent, trustworthy, and meaningful. In healthcare, AutoML and interpretability are already beginning to empower the clinical community by enabling the crafting of actionable analytics that can inform and improve decision-making by clinicians, administrators, researchers, policymakers, and beyond.
This talk presents state-of-the-art AutoML and interpretability methods for healthcare developed in our lab and how they have been applied in various clinical settings (including cancer, cardiovascular disease, cystic fibrosis, and recently Covid-19), and then explains how these approaches form part of a broader vision for the future of machine learning in healthcare.
Location and local date/time
This event will take place online on November 10 at 09:00-10:30 CET (08:00-09:30 GMT).
About the event
The AutoML Fall School will cover core topics of AutoML, covering basics, state-of-the-art approaches and hands-on sessions. Enthusiastic AutoML experts will present their diverse views on AutoML to ML practitioners, developers, research engineers, researchers and students.
The event is finished.
- Nov 10 2021
TimeNote: time is shown in GMT
- 08:00 - 09:30