This lecture is part of the Cornell CS4780 course on Machine Learning for Intelligent Decision Making, taught by Kilian Weinberger at Cornell University. It introduces the fundamental setup of supervised learning, which is one of the most important paradigms in modern machine learning.

The course explains how supervised learning works by using labeled datasets, where each input example is paired with a correct output. The goal is to learn a function that maps inputs to outputs in a way that generalizes well to unseen data. This lecture builds the mathematical and conceptual foundation needed for understanding more advanced machine learning topics later in the course.

Students are introduced to core ideas such as training data, hypothesis space, model generalization, and decision-making frameworks. The lecture also emphasizes how machine learning is used in real-world decision systems and why proper problem formulation is critical for building effective AI models.

This is the first lecture in the CS4780 series, which is designed for students interested in artificial intelligence, data science, and computational learning theory. It provides a strong theoretical base for anyone planning to study machine learning at an advanced level.