If you are working in or interested in Machine Learning, this event is all about using practical machine learning techniques and how you can start using them at your company immediately. If your company has heard of machine learning and want to start using it at your company but don't know where to start then this is the event for you. With practical guides and advice coupled with access to consultants that can help your business realize the power and business value of using Machine Learning techniques.
17:00-17:10 - Meet’n’Greet - find a seat
17:10-17:15 - Brief welcome speech by Mikkel Nielsen (CluedIn ApS)
17:15-18:00 - Rasmus Hvingelby - “Summarizing Documents using Deep Learning” - followed by questions
18:00-18:30 - Break with pizza and drinks
18:30-19:15 - Yevgeny Seldin: Online Machine Learning followed by questions
19:15-19.30 - Q&A session followed by a short outro from Mikkel Nielsen (CluedIn ApS)
Rasmus Hvingelby is a Research Assistant at the Technical University of Denmark where he works in the section for Cognitive Systems. He recently graduated as a Msc in Computer Science and Engineering writing his thesis on Text Summarization using Deep Learning. Rasmus is interested in using machine learning to solve real world problems and has focused on using deep learning for both image processing as well as natural language processing. with a focus on Artificial Intelligence.
Yevgeny Seldin is an Associate Professor at the University of Copenhagen. He is working in machine learning with particular interest in online learning and PAC-Bayesian analysis. Prior to arriving at the University of Copenhagen he has held positions at UC Berkeley in the USA, Queensland University of Technology in Australia, Max Planck Institute in Germany, and University College London in the UK. He holds PhD from the Hebrew University of Jerusalem in Israel. Yevgeny has over 20 scientific publications and 3 patents in the field of machine learning. For more information about his research see https://sites.google.com/site/yevgenyseldin/.