Generalized linear models (GLMs) allow us to solve different types of supervised learning problems using the same basic framework (linear combination of predictors, model the outcome as a probability distribution, use maximum likelihood estimation to fit). Linear and logistic regression are examples of GLMs; we introduce Poisson regression as a third example. We talk about how the models are fit using the analogy of hill climbing. Key terms: link function, gradient ascent, Fisher scoring. (25:13; 14 pages)