Winter Term 2023/2024: Self-Supervised Learning
Over the course of the semester, we will explore key concepts and methods used in self-supervised learning. Topics include contrastive learning, autoencoders, generative models, transformers, and other techniques for learning from unlabelled or partially labeled data. The course will start with a review of basic concepts in machine learning and probability theory, followed by an introduction to self-supervised learning and its advantages over traditional methods. We will then review various Self-Supervised Learning algorithms such as BYOL, SimCLR and IGEPA. Finally, we will discuss applications of self-supervised learning in computer vision, natural language processing, and reinforcement learning.
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