Cagnetta, FrancescoOliveira, DeborahSabanayagam, MahalakshmiTsilivis, NikolaosKempe, Julia2025-01-252025-01-252025-01-252024-10-3110.1088/1742-5468/ad292c2-s2.0-85208386159https://infoscience.epfl.ch/handle/20.500.14299/244073Lecture notes from the course given by Professor Julia Kempe at the summer school ‘Statistical physics of Machine Learning’ in Les Houches. The notes discuss the so-called NTK approach to problems in machine learning, which consists of gaining an understanding of generally unsolvable problems by finding a tractable kernel formulation. The notes are mainly focused on practical applications such as data distillation and adversarial robustness, examples of inductive bias are also discussed.falsedeep learninglearning theorymachine learningtext::journal::journal article::research article