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Foundations of Machine Learning 1.2: Framing the Learning Problem (Prof. Mohri, NYU)
Автор: SodiumMan
Загружено: 2025-05-31
Просмотров: 124
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In Part 2, we dive into the formal “learning problem” that underpins every ML algorithm. You’ll learn:
The difference between supervised, unsupervised, semi-supervised & transductive learning
Batch vs. online learning paradigms, and active vs. passive queries
How we define and measure generalization error, empirical error, and the Bayes error
Why overfitting happens—and the trade-off between model complexity and sample size
By the end, you’ll be able to state precisely what it means for an algorithm to “learn” and why that’s so challenging in practice.
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