Artificial Intelligence - Methods and Applications 7.5 credits

About the course

Modern intelligent systems are transforming the daily life of society. These kind of systems are designed and implemented considering Artificial Intelligent (AI) models and algorithms. This course aims to present different AI theories and algorithms in order to give a solid background in the area, as well as practical knowledge about how to implement real intelligent  systems. The main theme of the course is theories and algorithms from classical AI. During the course, the students will acquire knowledge about different paradigms of AI, e.g. logic-based and data-driven methods as well as rational intelligent agents.
 
The course consists of two parts:
Part 1, theory, 4.5 credits
Topics covered:

• Answer Set Programming (ASP), e.g., stable models, optimization modelling.
• Knowledge representation, e.g., description logics.
• Probability theory, e.g., axioms, conditional probability, Bayes' rule.
• Probabilistic reasoning, e.g., Bayesian networks.
• Probabilistic reasoning over time, e.g., hidden markov models.
• Sequential decision making, e.g., markov decision processes, stochastic planning.
• Reinforcement learning.
• Multi-criteria decision-making, e.g., utility functions, evaluation of alternatives, pareto-optimality.

Part 2, practice, 3 credits.
In the laboratory part some of the theories and techniques discussed in the theoretical part are put into practice.