Foundation Models (engl.)
Foundation models also known as large X models are machine learning or deep learning models trained on a large set of multi-modal data. These models can be generative and applicable to a wide range of use cases. In this regard, both research and industrial communities are investing more and more on resources and training strategies demanded by the foundation models. Despite of the resource-intensive nature of the foundation models, the multi-modality of their training data and their tendency to build generic generative models allow them to be applied to various perception, prediction, and decision-making applications. The potentials of the foundation models not only lies on their large multi-modal data but also on their architectures, objective functions, and training strategies.
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Requirements: Familiarities with deep neural networks, multi-modal data, and training strategies are helpful but not necessarily needed. Please bring your own laptop.
Credit Points (ECTS): –
Dr.-Ing. Faezeh Fallah
Faezeh Fallah obtained her bachelor of science degree in electrical engineering with a specialization on telecommunication engineering in 2006. From 2006 up to 2011 she has worked as a designer of radio frequency heads of commercial telecommunication systems based on DVB standards. In 2011–2014 she finished her master of science degree on information technology at the university of Stuttgart and in 2014–2017 she pursued her PhD (Dr.-Ing.) degree in the faculty of electrical engineering and computer science of the university of Stuttgart in the area of artificial intelligence and processing of magnetic resonance images. Since 2017, she has been a research engineer developing algorithms based on artificial intelligence for processing and synthesis of sensor data in the automotive industry.
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