Linear Digressions is a podcast that touches upon the technical and social aspects of machine learning and data science. If you are interested in learning about the actual practice of machine learning, you can listen to episodes such as “Convolutional Neural Networks,” “Gaussian Processes,” “Better know a distribution: the Poisson distribution,” as well as many others. If you are interested in learning about the societal impacts of machine learning, tune into “Racism, the criminal justice system, and data science,” “Facial recognition, society, and the law,” “Interesting technical issues prompted by the GDPR and data privacy concerns,” or “Differential Privacy: how to study people without being weird and gross.” Episodes are short and digestable, ranging anywhere from 15 to 35 minutes, and are riddled with witty humor, interesting insights, and information from experts.