Deep learning has seen tremendous successes in areas such as image recognition and natural language processing – but its adoption in finance has been slow.
Financial data is often plagued with a notoriously low signal-to-noise ratio as well as non-stationarity of the data. Applying deep learning techniques to financial time-series data thus often requires bespoke models.
The question is how.
At this interactive event, Stefan Zohren – an Associate Professor (Research) at the Machine Learning Research Group and the Oxford-Man Institute for Quantitative Finance (OMI) – will explore recent work on applying deep learning techniques to financial microstructure data. The importance of uncertainties as obtained from Bayesian deep learning approaches is pointed out and several similarities to other areas such as self-driving cars and modelling of climate data are highlighted.
Join Hewlett Packard Enterprise, Nvidia and select group of financial services industry- and thought leaders for one unique evening of peer-to-peer discussion about modern advances in the application of deep learning for finance.
6:30 – 6:40 pm – Welcome Reception
6:30 – 6:40 pm – Introduction from Master of Ceremonies
6:40 – 7:00 pm – Keynote speaker: Stefan Zohren
7:00 – 9:00 pm – Three-course dinner complemented with table discussion points
9:00 – 9:30 pm – Wrap up from table champions and closing remarks from host
- Deep learning in finance: what are the key considerations?
- Deep learning in fraud detection: how are we using AI to enhance security and safety?
- Trust in AI: can deep learning improve customer experience?
Stefan Zohren Associate Professor at University of Oxford
Experienced researcher with an extensive academic background in theoretical physics, statistics and machine learning. Involved in cutting-edge research projects primarily at the University of Oxford and in cooperation with industry partners such as Nokia Technologies, Lockheed Martin and Man Group. Working on machine learning, in particular with applications in finance, both in academia as well as industry.
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