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

Maurizio Parton

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
  • For further information, please contact Prof. Luigi Amedeo Bianchi
  • Venue: Webinar, credentials will be sent to the participants the day before of the event
  • Language: English

Material (Restricted access, user: RL2021)

Further References

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