Machine learning now powers a huge range of applications, from speech recognition systems to search engines, self-driving cars, and prison-sentencing systems. Many applications that were once designed and programmed by humans now combine human-written components with behaviors learned from data. This shift presents new challenges to computer science (CS) practitioners and educators. In this column, we consider how machine learning might change what we consider to be core CS knowledge and skills, and how this should impact the design of both machine learning courses and the broader CS university curriculum. Computing educators1,6 have historically considered the core of CS to be a collection of human-comprehensible abstractions in the form of data structures and algorithms.