Csaba Szepesvari, PhD

Professor, Faculty of Science - Computing Science

Personal Website: https://sites.ualberta.ca/~szepesva/


Professor, Faculty of Science - Computing Science


Area of Study / Keywords

Learning theory machine learning reinforcement learning online learning bandits



  • M.Sc., Mathematics, Jozsef Attila University, 1993
  • M.Sc., Computer Science, Jozsef Attila University, 1993
  • Ph.D., Probability and Statistics, Jozsef Attila University, 1999



Artificial Intelligence

Machine Learning

Reinforcement Learning


Traditional programming fails miserably when computers need to interact with the `real-world'. Examples include a robot whose mission is to explore Mars or to clean up a room, an algorithm that needs to decide if a person should be given credit, or a chat-bot conversing with humans in English. Artificial intelligence is the science whose aim is to create computer programs that are able to cope with problems like these.

A core tool of artificial intelligence is machine learning. Machine learning allows computers to learn from data. This way computers can discover solutions to difficult problems on their own.

My research focuses on creating smart, efficient learning algorithms. I am working on developing better learning algorithms and understanding what makes efficient learning possible. I am particularly interested in problems when a machine continuously interacts with its environment while trying to discover autonomously a good way of interacting with it.

Prospective students should look at my personal homepage.