Meta-Learning Essentials - A Hands-On Approach

This session introduces meta-learning—’learning to learn’—which focuses on creating models that can quickly adapt to new tasks by leveraging prior knowledge. Participants will explore the fundamental concepts of meta-learning and examine key techniques such as optimization-based methods like MAML, metric-based approaches like Prototypical Networks, and model-based strategies like HyperNetworks. Additionally, the tutorial includes a hands-on session where attendees will use specialized libraries to implement and experiment with these meta-learning algorithms, providing practical experience in designing adaptive and efficient machine learning models.

• Date: February 14, 2025
• Time: Theory: 14:00 - 14:50 / Coffee 14:50 - 15:10 / Lab 15:10 - 16:00
• Location: Nancy-Salle A008 Jean Legras
• Instructor: Omar Anser, Ph.D. Candidate
• Link to Sign-Up: Register Here