E05. An Overview of Semi-Supervised Learning

Episode 5: An Overview of Semi-Supervised LearningWhat do you do when you have a mountain of data, but only a handful of labels? In this episode, we explore Semi-Supervised Learning, the hybrid approach that solves the "labeling bottleneck" by combining the best of both worlds.In this episode, we cover:The Problem: Why labeling data (especially for medical or complex tasks) is too expensive and slow to rely on alone.The Hybrid Solution: How models use a small set of "Ground Truth" to guide their learning from a massive ocean of unlabeled data.The 4 Key Assumptions: The logic that makes this possible, including the Cluster, Smoothness, Low-Density, and Manifold assumptions.Core Techniques:Transductive Learning: Using Label Propagation and Active Learning to fill in the blanks.Inductive Learning: Building robust models via Self-Training (where the AI grades its own homework) and Co-Training (getting a second opinion).Next Episode: We clarify the confusion between this and our next topic: Self-Supervised Learning.