Summer School on Learning with Temporal Point Processes
In recent years, there has been an increasing number of machine learning models, inference methods and control algorithms using temporal point processes (TPPs).
They have been particularly popular for understanding, predicting, and enhancing the functioning of social and information systems, where they have achieved unprecedented performance. In this course, you will learn about these recent advances.
The first part of the course will provide an introduction to the basic theory of temporal point processes, revisit several types of points processes, and introduce advanced concepts such as marks and dynamical systems with jumps.
The second and third parts of the course will explain how temporal point processes have been used in developing a variety of recent machine learning models and control algorithms, respectively. Therein, it will revisit recent advances related to, e.g., deep learning, Bayesian nonparametrics, causality, stochastic optimal control and reinforcement learning. The course will consists of both theory and coding (i.e., python) sessions.
Dates: August 13-15th.
Place: St. Petersburg, Times Business Center, Kantemirovskaya 2.
Tutor: Manuel Gomez Rodriguez, PhD.
Participation is free.
Maximum number of participants: 30.
Application deadline: June 15th.
Notification of acceptance: June 30th.
Apply via this form.
If you have any question, email to firstname.lastname@example.org.
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13-15 August 2019Summer School on Learning with Temporal Point Processes