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The aim of the course is to familiarise the student with the basic methods and techniques in the field of artificial intelligence and autonomous systems, with particular emphasis on practical use in the development of software for data science problems.

The course includes the following elements:

- Recommendation systems: user- and content-based recommendations, recommendation algorithms (such as neighborhood-based, collaborative filtering and matrix factorisation), context-aware recommendations, cold start, eliciting/implicit ratings, evaluation and metrics.
- Information retrieval, knowledge acquisition, knowledge representation and reasoning, the semantic web, constructing and querying knowledge graphs, extracting data from online sources and source alignment
- Probabilistic models and decision theory, decision making under uncertainty, optimisation, dynamic programming, methods for adversarial and heuristic search
- Practical methods for data mining
- Machine learning for both supervised and unsupervised learning. Algorithms for classification, prediction, and clustering.

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