How do machines learn to make smart decisionsโand how can we use them in practice? This course introduces reinforcement learning, where algorithms learn from feedback over time. Useful for tasks involving repeated interaction, RL powers applications from robotics to business, economics, and the social sciences.
Attendance is free.
Struggling to turn data into valuable insights?
Wondering how to assess AI-generated insights?
Finding it tough to choose the right algorithms and methods?
Then join the ๐๐ฐ๐ผ๐ป๐๐ฎ๐๐ฎ ๐ช๐ผ๐ฟ๐ธ๐๐ต๐ผ๐ฝ on ๐๐๐ป๐ฒ 23-24 at the University of Mannheim for practical, actionable insights. No prerequisites – just bring your questions and leave with fresh strategies.
Attendance is free.
With the rise of deep learning and abundant text data, NLP has advanced rapidly – but adapting models to specific tasks still requires annotated data and expert insight. Active Learning tackles this by involving humans strategically in the learning loop, selecting the most informative examples to label. This makes model training more efficient and effective with fewer annotations.
On June 26th in Kassel, you’ll be introduced to Human-in-the-Loop Learning and (Deep) Active Learning, and design your own Active Learning cycle in Python during a hands-on session.
Attendance is free.
The BERD Academy offers a range of self-paced courses designed to empower learners with essential data science and machine learning skills on their own schedule. These include foundational topics like “Introduction to Machine Learning (I2ML)”, which provides a comprehensive introduction to supervised learning, and practical programming courses such as “Data Science with Python” and “Data Science with R” for hands-on coding experience. Advanced learners can explore cutting-edge fields through courses like “Deep Learning for NLP” and “AutoML”, featuring state-of-the-art algorithms and techniques. Each course includes rich resources such as video lectures, slides, exercises, and quizzes, ensuring an engaging and flexible learning experience.
