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Machine learning and symbolic knowledge representation

Machine learning is a very popular domain of computer science that allows for learning from data models for classification, prediction or learning a strategy in unknown environments. The model or the strategy can be expressed using many methods. 

Lately, non-symbolic knowledge representation in a form of a (deep) neural network is very popular because it is very effective. However, there are applications in which interpretability of models is more important than a top accuracy. In this presentation examples of such cases together with algorithms using symbolic knowledge representation that can be applied will be discussed.

Bartlomiej Śnieżyński received his Ph.D. and habilitation degrees in Computer Science from AGH University of Science and Technology in Krakow, Poland where has been a faculty since 1998. In 2004 he worked on applications of rule learning as a Postdoctoral Fellow under the supervision of Professor R. S. Michalski at the Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA, USA. 

Currently, he is an associate professor in the Department of Computer Science at AGH. His research interests include machine learning, reasoning and knowledge engineering.

Bartłomiej Śnieżyński

Institute of Computer Science AGH, Krakow


Monday, 13 December 2021, 2:00-3:30 PM (CET)

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