Events Calendar

Welcome to your calendar for data science events! Dive into a curated list of courses, conferences, seminars, workshops, and key deadlines. Tailor your search to match your interests by adjusting the event category filters. For those specifically looking for PhD courses, simply modify the filter settings to include these events as well. Stay connected and up-to-date with the latest in data science, all in one place.


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  • Methods of AI in Modern Data Management Systems (2026)

    Welcome Methods of AI in Modern Data Management Systems Description: This course is at the intersection of AI and data system design. The basic idea is to learn where the application (!) of AI methods may benefit the design of specific components of a data management system. The students will learn about opportunities as well as […]

  • Methods of AI in Modern Data Management Systems (2025)

    Welcome Methods of AI in Modern Data Management Systems Description: This course is at the intersection of AI and data system design. The basic idea is to learn where the application (!) of AI methods may benefit the design of specific components of a data management system. The students will learn about opportunities as well as […]

  • Introduction to Probabilistic Machine Learning

    Welcome to Introduction to Probabilistic Machine Learning Description: Machine learning (ML) and artificial intelligence have had major impacts on all areas of society and across research disciplines. Probabilistic ML provides a principled approach, based on probabilistic methods, to develop intelligent systems that make optimal decisions under uncertainty. Many problems in science can be casted as decision […]

  • Reinforcement Learning

    Welcome to Reinforcement Learning Description: An intelligent system is expected to generate policies autonomously to achieve a goal, which is mostly to maximize a given reward function. Reinforcement learning is a set of methods in machine learning that can produce such policies. To learn optimal actions in an environment that is not fully comprehensible to itself, […]