HSE SLT lecture 6: The agnostic setting and risk bounds

Measure concentration: Markov, Chebeyshev, Hoeffding bounds. Risk, bias optimal classifier, risk decomposition. Risk bounds for finite classes and bias complexity trade-off. Risk bound using VC-dimension (the proof is similar as in previous lecture, it will be for homework) Fundamental theorem of statistical learning theory. Course website: http://wiki.cs.hse.ru/Statistical_learning_theory_2025