I studied computer engineering (B.Sc.) and Automation & IT (M.Eng.). Generally, I am interested in machine learning (ML) approaches (in the broadest sense), but particularly in the fields of time series analysis, anomaly detection, Reinforcement Learning (e.g. for board games), Deep Learning (DL) and incremental (on-line) learning procedures.
S. Bagheri, M. Thill, P. Koch, and W. Konen, “Online Adaptable Learning Rates for the Game Connect-4,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 8, no. 1, pp. 33–42, Jan. 2016.
M. Thill, P. Koch, and W. Konen, “Reinforcement learning with n-tuples on the game Connect-4,” in Proceedings of the 12th International Conference on Parallel Problem Solving from Nature (PPSN), 2012, pp. 184–194.
M. Thill, “Using n-tuple systems with TD learning for strategic board games
(in German),” Cologne University of Applied Science, CIOP Report 01/12, 2012.
M. Thill, S. Bagheri, P. Koch, and W. Konen, “Temporal Difference Learning with Eligibility Traces for the Game
Connect Four,” in Proceedings of the 2014 IEEE International Conference on Computational Intelligence and Games (CIG), 2014, pp. 586–591.
M. Thill and W. Konen, “Connect-4 Game Playing Framework (C4GPF).” 2014.
S. Bagheri, M. Thill, P. Koch, and W. Konen, “Online Adaptable Learning Rates for the Game Connect-4,” CIplus, TR 03/2014, Jan. 2014.