Lecture 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.

翻译：这是由朱莉娅·肯普教授在莱苏什举办的“机器学习的统计物理学”暑期学校授课的讲稿。该讲稿讨论了对机器学习问题进行所谓NTK（神经切线核）方法的研究，这种方法通过寻找可处理的核形式来理解通常不可解的问题。讲稿主要关注数据蒸馏和对抗鲁棒性等实际应用，同时讨论了归纳偏置的示例。