Self-Supervised Learning: How Machines Learn Without Labels

Labels are the bottleneck of deep learning, and self-supervised learning is how models learn from unlabeled data instead, the approach Yann LeCun calls the dark matter of intelligence. This cursus builds it from the ground up: pretext tasks and the shift to joint embeddings, the collapse problem that lurks underneath, contrastive methods that fix it with negatives (SimCLR, MoCo), and the negative-free families that fix it without them (BYOL, SimSiam, VICReg, Barlow Twins, MAE), with the through-line to JEPA and the energy-based view.