We all are aware of how machine learning has revolutionized our world and has made a variety of complex tasks much easier to perform. In this event, we will talk about how the size of the data set impacts Machine Learning algorithms, how deep learning model performance depends on data size and how to work with smaller data sets to get similar performances.
8.00 - 8.05 Introduction
Michael Nilsson, Applied AI DIH North project manager, Luleå University of Technology
8.05 – 8.15 A real life Machine Learning challenge
Niklas Karvonen, CTO, Substorm
8.15 – 8.45 Machine Learning with few data sets, State-of-the-art & solutions
Marcus Liwicki, Professor of Machine Learning at Luleå University of Technology, and his team
8.45 – 8.55 Q&A session
For those of you who want to discuss more in detail stay, and/or let us know so we can contact you afterwards.
Register to the event:
The Zoom link will be sent to all registered the day before.
About Niklas Karvonen
Niklas is CTO at Substorm and a machine learning expert who has a long experience of software development ranging from embedded systems to web. He has a PhD in Pervasive and Mobile computing, specialization: machine learning on resource-constrained systems.
About Marcus Liwicki
Marcus Liwicki is chaired professor at Luleå University of Technology and a senior assistant in the University of Fribourg. His research interests include machine learning, pattern recognition, artificial intelligence et cetera. Marcus is a co-author of the book "Recognition of Whiteboard Notes – Online, Offline, and Combination”.
About Applied AI DIH North
The project Applied AI DIH North aims to create a strong innovation system for growth in the AI industry, a Digital Innovation Hub as a base, in collaboration, research, innovation, applied test-driven development, education and clustering. The project is funded by the EU's regional development fund, Luleå University of Technology, Luleå municipality, Skellefteå municipality and Region Norrbotten. Read more at https://www.aidih.se/en-US
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