This chapter provides an overview of the types of inference problems we address and the different approaches to solving them. We focus on risky inference: drawing conclusions, learning and making predictions in situations where certainty is impossible. Predicting a response from one or more predictors using past data is called supervised learning. When the response is continuous, the task is regression; when it is categorical, the task is classification. In unsupervised learning, there is no response variable. Instead, the goal is to find patterns or structure in data, as in density estimation, clustering and dimensionality reduction. In both supervised and unsupervised contexts, overfitting occurs when we model data in excessive detail and fail to distinguish systematic patterns from noise; underfitting occurs when our models are too simple to capture systematic patterns. Probability is a key tool for tackling risky inference, with frequentist and Bayesian interpretations motivating distinct approaches. Finally, large neural networks have proven remarkably effective in both supervised and unsupervised tasks, often avoiding overfitting despite containing billions of parameters.