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.


+ Add your own event

 

D-Pop 2024: Problemløsning og programmering

IT-University of Copenhagen Rued Langgardsvej 7, Copenhagen, Denmark

Kan du lide at løse problemer sammen med andre, og kan du programmere (en smule)? D-Pop er et landsdækkende arrangement i holdbaseret problemløsning og programmering. Vi har lavet en masse sjove programmeringsopgaver, som I kan gå ombord i, og vi hjælper jer også gerne undervejs, hvis der skulle blive brug for det. Kom alene, snup […]

Free

Introduction to programming in R for biologists

Copenhagen København, Denmark

Aim and contentPlease note that to apply, you have to both apply via this site AND send an email. Please see the information on how to apply at the bottom of this page.-----------------Programming (particularly in R) has become an essential tool in biological research, including PhD projects. But biology degrees often do not include an actual […]

Machine learning in health technology (2024)

Aalborg University TBA Aalborg

Welcome to Machine learning in health technology Program: BEN Description: Do you want to get into machine learning but do not know where to start? This is a 3-day course with a practical approach to machine learning directed to PhD students at the Faculty of Medicine. The course includes two days of lectures from basics about machine learning to […]

Self Supervised Learning (2024)

Aalborg University TBA Aalborg

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 of single- and multiple-modality data, covering deep architectures, training target and loss functions used in state-of-the-art methods, and selected downstream applications. A focus will be […]