The course covers algorithms for reasoning and decision making under uncertainty, including Probabilistic Graphical Models and inference algorithms, Hidden Markov Models, Decision Networks, Markov Decision Processes, Partially Observable Markov Decision Processes, Reinforcement Learning and Game Theory.

The course covers the fundamentals of neural networks, including perceptrons, feed-forward networks, convolutional neural networks, as well as recent deep generative models, including Generative Adversarial Nets, Variational Autoencoders and Transformers.

This course provides an introduction to classical and modern artificial intelligence, including search-based problem solving, reasoning under uncertainty, supervised and unsupervised machine learning, reinforcement learning. The course is structured as a block seminar and given at UBB Cluj-Napoca, Romania

We are always looking for motivated students who want to work on interesting and challenging topics as Bachelor or Master theses.