]> 2026-04-04T12:04:16+02:00 Representation Learning for Deviation Detection 0 en Master Optimise Echo State Networks for time series forecasting and reconstruction. Propose methods, e.g. objective functions, to train Echo State Networks for deviation detection Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms Bengio, Yoshua, Aaron Courville, and Pascal Vincent. "Representation learning: A review and new perspectives." IEEE transactions on pattern analysis and machine intelligence 35.8 (2013): 1798-1828. Jaeger, Herbert. "Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the" echo state network" approach. GMD-Forschungszentrum Informationstechnik, 2002. Jaeger, Herbert, et al. "Optimization and applications of echo state networks with leaky-integrator neurons." Neural networks 20.3 (2007): 335-352. Lukoševičius, Mantas. "A practical guide to applying echo state networks." Neural networks: Tricks of the trade. Springer Berlin Heidelberg, 2012. 659-686. Wang, Lin, Zhigang Wang, and Shan Liu. "An effective multivariate time series classification approach using echo state network and adaptive differential evolution algorithm." Expert Systems with Applications 43 (2016): 237-249. Li, Decai, Min Han, and Jun Wang. "Chaotic time series prediction based on a novel robust echo state network." IEEE Transactions on Neural Networks and Learning Systems 23.5 (2012): 787-799. Krause13, André Frank, et al. "Evolutionary Optimization of Echo State Networks: multiple motor pattern learning." (2010). Marco Rigamonti et al., "Echo State Network for the Remaining Useful Life Prediction of a Turbofan Engine." Third European Conference of the Prognostics and Health Management Society 2016, Bilbao, Spain, 5-8 July, 2016. PHM Society, 2016. Chen, Huanhuan, Peter Tiňo, and Xin Yao. "Cognitive fault diagnosis in Tennessee Eastman Process using learning in the model space." Computers & Chemical Engineering 67 (2014): 33-42. Quevedo, Joseba, et al. "Combining learning in model space fault diagnosis with data validation/reconstruction: Application to the Barcelona water network." Engineering Applications of Artificial Intelligence 30 (2014): 18-29. Fan, Yuantao, et al. "Predicting Air Compressor Failures with Echo State Networks." Third European Conference of the Prognostics and Health Management Society 2016, Bilbao, Spain, 5-8 July, 2016. PHM Society, 2016. Open Fall 2019 Representation Learning for Deviation Detection 2019-09-09T20:20:11Z 2458736.3473495 Representation Learning for Deviation Detection 0 [2,"smw-datavalue-property-restricted-annotation-use","Keywords"] Representation Learning for Deviation Detection# ERRd9c33d5ac8a89308063e13c4b212fa7c