It is a poorly publicized fact that, in addition to the basic science courses and clinical rotations that they must do during their training, physicians also take courses in biostatistics and medical decision making. In these courses, prospective physicians learn some math and statistics that will help them as they sort through different symptoms, findings, and test results to arrive at diagnoses and treatment plans for their patients. Many physicians, already bombarded with endless medical facts and knowledge, shrug these courses off. Nevertheless, whether they learned it from these courses or from their own experiences, much of the reasoning that physicians use in their daily practice resembles the math behind some common machine learning algorithms. Let's explore that assertion a bit more in this section as we look at some popular frameworks for medical decision making and compare them to machine learning methods.