Syllabus
The subject aims at deepening of the knowledge from the following areas with the focus on their application to real data from different competitions (e.g. Kaggle, conference competitions, …).
Metalearning - model selection, hyper-parameter tuning (grid search, evolutionary algorithms), ensembles (bagging, boosting, stacking, blending)
Combination of evolutionary algorithms and machine learning - surrogate modelling; hybrid models, relation between local and global search, memetic algorithms; evolution and metalearning
Advanced evolutionary computing - CMA-ES, constrained optimization
Kernel methods - support vector machines (classification, regression), kernel neural networks, Radial Basis Function Networks
Semi-supervised learning - self-learning, generative models, Semi-Supervised Support Vector Machines, graph-based methods
Advanced models of neural networks - Echo State Network, Long Short Term Memory Network, autoencoders, convolution networks, Boltzmann machines, deep networks
Annotation
Introduction of modern computational intelligence methods (evolutionary algorithms, machine learning and related fields) and their application to solving of real problems. Basic knowledge of machine learning, neural networks and evolutionary algorithms is required.