The Big Data School is both a theoretical and a practical scientific event, focusing on a deep theoretical background and skills development through practical workshops. The event is aimed at industry practitioners, researchers, PhD students and postgraduates. The event offers participants the opportunity to gain knowledge on a wide range of topics related to big data through interaction with experts in the field. This knowledge is reinforced by developing application skills through practical sessions.
About tree-based models - Decision Trees, Random Forests, Boosted Trees - which are among the most popular methods for learning predictive models from data. To work with large datasets, and how to use interpretation methods. About feed-forward neural and recurrent neural networks (RNNs) and LSTM networks. How to apply RNNs for natural language processing (sentiment analysis; question and answer analysis) and text generation. To work in Python and learn about time series. How to classify data using a Support vector machine (SVM).
phone: +370 37 300 303
email alina.kotkeviciene@ktu.lt
Dr. Mindaugas Kavaliauskas (KTU Faculty of Mathematics and Natural Sciences, Lithuania) – received his degree of Ph.D. in Mathematics (2005). He is currently associate professor at KTU. He is giving lectures on mathematical statistics, time series analysis, and data mining. His fields of scientific interests are multivariate data analysis, statistical models, applied mathematics, and machine learning.
Dr. Tomas Iešmantas (KTU Faculty of Mathematics and Natural Sciences, Lithuania) – is currently associate professor at KTU. His main research areas are application of deep learning methods in medical diagnostics, as well as using AI-based methods for various industry applications. The researcher teaches graduate students Mathematical Methods of Artificial Intelligence, Data Analysis, Data Mining Methods, Machine Learning Methods and Programming for Data Processing and Visualization.
Dr. Michael Fairbank (University of Essex, UK) – is a Computer Science lecturer at the University of Essex, UK. He is an active machine-learning researcher, with publications in reinforcement learning, deep learning and neural networks. In his previous careers he worked as a computer consultant and as a mathematics teacher. He has a passion for all things related to computing, mathematics and AI.
Dr. Wannes Meert (KU Leuven, Belgium) – received his degrees of Master of Electrotechnical Engineering, Micro-electronics (2005), Master of Artificial Intelligence (2006) and Ph.D. in Computer Science (2011) from KU Leuven. He is currently research manager in the DTAI research group at KU Leuven. His work is focused on applying machine learning, artificial intelligence and anomaly.
K. Donelaičio St. 73
44249 Kaunas, Lithuania
phone: +370 (671) 36 146
email mvg@ktu.lt
