Selected Topics in Artificial Intelligence

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

Hastie T., Tibshirani R., Friedman J. (2009) The elements of statistical learning, 2nd edition, Springer. COBISS.SI-ID – 40339554
Andrienko N., Andrienko G. (2006) Exploratory Analysis of Spatial and Temporal Data, Springer. https://link-springer-com.nukweb.nuk.uni-lj.si/book/10.1007/3-540-31190…
Forsyth D.A., Ponce, J. (2011) Computer Vision: A Modern Approach, Prentice Hall https://link-springer-com.nukweb.nuk.uni-lj.si/book/10.1007/978-3-540-8…