• Concepts of intelligent system design.

• Applications of IS

• Implementation of IS components in modern tools (e.g. ‘R’)

• Data acquisition for IS

• Infrastructure for intelligent systems.

• Advanced knowledge representations and machine learning techniques:

o Symbol-based methods,

o Connectivist methods,

o Association rules,

o Bayes classifiers,

o Hybrid methods.

• Transition from classic connectivist methods to deep learning

• Upgrading knowledge models

• Prediction in dynamic systems and chaos theory.

• Cellular automata.

• Nature-based intelligent systems.

• Evaluation, ethical questions and challenges.

• Functional safety for IS