Syllabus outline: selected chapters in the following
topics:
reasoning and decision making under uncertainty,
machine learning (advanced classification and
clustering, learning in dynamical systems, learning in
weakly structured domains, learning of spatially and
temporally defined data),
data mining and visualization of data and models,
ensemble methods in data analyitic
comprehensible machine learning: (soft) rules, subgroup
discovery, asociation rules, explanation of decision
models
natural language processing and text mining
data fusion
matrix factorization methods in data mining
learning from data streams
estimation of prediction reliability
biologically motivated architectures of artificial intelligence
applications of artificial intelligence (e.g., bio-medicine, biometrics, ecology, business applications, ...).
advanced models for image interpretation
optimization methods for inference in computer vision
