Artificial Intelligence (MSc)

The course covers the most fundamental concepts, modeling approaches, and resolution methods of classical AI including intelligent agents' architectures, uninformed and informed search, adversarial search, integration of search and machine learning, bayesian networks, planning, constraint satisfaction programming, and state-of-the-art metaheuristic search methods (e.g., simulated annealing, genetic algorithms, genetic programming, probabilistic model-building genetic algorithms also known as estimation of distribution algorithms, multi-objective evolutionary algorithms, and ant colonies).

This course introduces the students to basic problems, models, and techniques of Artificial Intelligence (AI) and enables them to model and solve specific AI problems. 

In presence

Lectures

Evaluation will be based on a closed-book written test that comprises open questions and numerical problems.