A hacking day of Mathematics for Data Science at the Department of Mathematics

# Maurizio Parton

Department of Economics,
University of Chieti-Pescara
Bio I am a Professor in Mathematics and I teach Mathematics and Machine Learning at the University of Chieti-Pescara. My main research topic is Differential Geometry, but when AlphaGo first appeared I was fascinated by the potential of Artificial Intelligence. I have delved deeply in the theory of Neural Networks and Reinforcement Learning, and started working on many research projects applying AI to games, computer science and biology. I am deeply convinced that Artificial Intelligence will mark our (very) near future, and for this reason I am passionately dedicated to the dissemination of its fundamental principles and basic techniques.

## Syllabus

Reinforcement Learning (RL) is a machine learning technique where an agent learns to solve a decision problem by performing actions and assessing their results.
RL has been acknowledged as a breakthrough technology by MIT in 2017. We will study the fundamentals of RL and will sketch the latest methods used to solve a variety of complex tasks, from gaming to computer science, finance, and robotics. This is a 12h crash course intended for students with a background in probability. The course is comprised of theory, applications and assignments. Some experience with Python may prove useful to full profit from the exercises and assignments.

## References

We will follow the textbook Reinforcement Learning: An Introduction, second edition, by Richard S. Sutton and Andrew G. Barto and the video lectures By David Silver, DeepMind

## Schedule

• Wednesday 27 January 2021 @ 14.00-17.00
• Friday 29 January 2021 @ 14.00-17.00
• Wednesday 03 February 2021 @ 14.00-17.00
• Friday 05 February 2021 @ 14.00-17.00

## Details

• Participation is free, however a notification by email to Prof. Luigi Amedeo Bianchi is mandatory
• Venue: Webinar, credentials will be sent to the participants the day before of the event
• Language: English