Montag, 17. Juli 2017

[HIForum] [Kolloquium] REMINDER: Informatics Colloquium Mo 17.07.17 at 17:15 in D-125 with Prof. Dr. Andreas Holzinger/ Medical University Graz

Dear all,
May we kindly remind you of the 
today's talk at 17:15 in room D-125. Please find the details below.
Best regards
Stephanie Schulte Hemming
 

Von: Schulte Hemming, Stephanie
Gesendet: Dienstag, 4. Juli 2017 13:14
An: kolloquium@informatik.uni-hamburg.de
Betreff: INVITATION: Informatics Colloquium Mo 17.07.17 at 17:15 in D-125 with Prof. Dr. Andreas Holzinger/ Medical University Graz

This is an invitation to the next Informatics Colloquium on Monday, 17 July 2017, 17:15, Campus "Informatikum/Stellingen", Room D-125. The talk entitled "Machine Learning and Knowledge Extraction: The challenge is in small amount of data sets" will be held by Prof. Dr. Andreas Holzinger, Professor at Medical University Graz, Institute for Medical Informatics/Statistics.

                

This talk will be held in English. The colloquium committee is looking forward to seeing you all there and to sharing this talk with you. For details on the series of colloquiums planned, please visit  https://www.inf.uni-hamburg.de/home/kolloquium/sose17.html

 

On behalf of the colloquium committee

Stephanie Schulte Hemming

Universität Hamburg

 

 

ABSTRACT:

The goal of Machine Learning is to learn from data, to extract and discover knowledge, and to help to make decisions under uncertainty. In automatic machine learning (aML) great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from "big data" with many training sets. However, sometimes we are confronted with a small amount of complex data sets, where aML suffers of insufficient training samples. The application of such aML approaches in complex application domains, e.g. as in health informatics seems elusive in the near future, and a good example are Gaussian processes, where aML (e.g. standard kernel machines) struggle on function extrapolation problems, which are trivial for human learners. In such situations, interactive Machine Learning (iML) can be beneficial where a human-in-the-loop helps in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where the knowledge and experience of human experts can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem reduces greatly in complexity through the input and the assistance of an human agent involved directly into the learning phase. Tackling such challenges needs a concerted effort, fostering integrative ML research between experts ranging from diverse disciplines, from data science to visualization, and both disciplinary excellence and a cross-disciplinary skill-set with international collaboration.

 

BIO:

Andreas Holzinger is lead of the Holzinger Group HCI–KDD, Institute for Medical Informatics/Statistics at the Medical University Graz, and Associate Professor of Applied Computer Science at the Faculty of Computer Science and Biomedical Engineering at Graz University of Technology. Currently, Andreas is Visiting Professor for Machine Learning in Health Informatics at the Faculty of Informatics at Vienna University of Technology. He serves as consultant for the Canadian, US, UK, Swiss, French, Italian and Dutch governments, for the German Excellence Initiative, and as national expert in the European Commission. Andreas obtained a PhD in Cognitive Science from Graz University in 1998 and his Habilitation (second PhD) in Computer Science from Graz University of Technology in 2003. Andreas was Visiting Professor in Berlin, Innsbruck, London (twice), Aachen, and Verona. Andreas and his Group work on extracting knowledge from data and foster a synergistic combination of methodologies of two areas that offer ideal conditions towards unraveling problems with complex health data: Human-Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the central goal of supporting human intelligence with machine learning to discover novel, previously unknown insights into data. To stimulate crazy ideas at international level without boundaries, Andreas founded the international Expert Network HCI–KDD. Andreas is Associate Editor of Knowledge and Information Systems (KAIS), Associate Editor of Springer Brain Informatics (BRIN) and Section Editor for Machine Learning of BMC Medical Informatics and Decision Making (MIDM). He is member of IFIP WG 12.9 Computational Intelligence, the ACM, IEEE, GI and the Austrian Computer Society. Home: http://hci-kdd.org

  

CONTACT:

Prof. Dr. Chris Biemann, Universität Hamburg, FB Informatik