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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Welcome to Biophysical Expression, Affect & Movement (BEAM) Organizer: Daniel Overholt Lecturers: Daniel Overholt (US/DK), Elizabeth Jochum (US/DK), Mark-David Hosale (US/CA), Alan Macy (US), Grisha Coleman (US), and Marco Donnarumma (DE/IT). ECTS: 3 Date/Time: 27, 28, 29 May 2024 Deadline: 06 May 2024 Max no. of […]
Welcome to Big Data Integration Organizer: Matteo Lissandrini, Katja Hose Lecturers: Giovanni Simonini, University of Modena and Reggio Emilia (Italy) ECTS: 2 Date/Time: June 2024 Deadline: 10 May 2024 Max no. Of participants: 20 Description: The course […]
Welcome to Data and Machine Learning Operations (DataOps and MLOps) Description: The course will cover central concepts, methods, techniques, and tools within DataOps and MLOps. Topics include Data Augmentation, Labeling, Cleaning, […]
Welcome to Applications of AI to Modern Data Management Systems Organizer: Professor Torben Bach Pedersen Lecturers: Professor Wolfgang Lehner ECTS: 2 Date/Time: 7-9 October 2024 Deadline: 16 September 2024 Max no. Of participants: 15 Description: The course […]
Welcome to Human-Centered Artificial Intelligence Organizer: Niels van Berkel Lecturers: Haiyi Zhu ECTS: 3 Date/Time: 4-5-6 November 2024 Deadline: 13 October 2024 Max no. Of participants: 20 Description: Artificial Intelligence has experienced a tremendous increase in attention in […]
Welcome to Self-Supervised Learning Organizer: Zheng-Hua Tan Lecturers: Zheng-Hua Tan ECTS: 2 Date/Time: November 18-20, 2024 Deadline: 28 October 2024 Max no. Of participants: 50 Description: The course gives an introduction to self-supervised learning methods for learning representations […]