Artificial Intelligence: Theory and Practice, 7,5 credits

This course provides an introduction to artificial intelligence (AI), building on prior knowledge in programming and algorithms. It covers fundamental concepts in traditional machine learning, including data handling, model evaluation, and issues such as bias–variance and overfitting, as well as a general overview of modern developments in generative AI and transformer-based models. The course combines theoretical foundations with practical applications, where students implement and compare different approaches while considering their limitations, risks, and ethical implications. It also introduces basic concepts in prompt engineering and agent-based systems, highlighting challenges related to the validation and reliability of AI-generated results.