In this Science Corner episode, a Nobel laureate guides us through the core distinction between discriminative models (learning P(Y|X) to draw decision boundaries) and generative models (learning P(X,Y) to understand the data and even generate new samples). We explore classic and modern methods, intuition with spam filtering, trade-offs like data needs and training challenges, and when each approach is the right tool for the job. A clear, accessible dive into how these perspectives shape modern AI.
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