This lecture introduces the fundamentals of supervised learning as part of Cornell University’s CS4780: Machine Learning for Intelligent Decision Making course. It is designed to help students understand how machines can learn from labeled data to make predictions and decisions.

Supervised learning is a core branch of machine learning where the model is trained using input-output pairs. The goal is to learn a mapping function that can accurately predict outputs for unseen data. This lecture lays the foundation for understanding how training datasets are structured, how models generalize, and why learning from examples is essential in artificial intelligence.

The course also explains key concepts such as hypothesis space, training error, and generalization error, which are critical for evaluating machine learning models. These ideas help learners understand how well a model performs not only on training data but also on new, unseen data.

Cornell CS4780 is widely recognized for its strong theoretical foundation in machine learning and decision-making systems. This lecture serves as the starting point for more advanced topics such as optimization, probabilistic models, and learning algorithms.

By the end of this introduction, learners gain a clear understanding of how supervised learning works and why it is one of the most important concepts in modern data science and artificial intelligence.