🎬AI Lectures6

Artificial Intelligence (AI) is the science of making machines do tasks that would need intelligence if a person did them. Today’s AI mostly focuses on specific tasks like recognizing faces or recommending products, which is called narrow AI. A future goal is general AI, which would do any thinking task a human can, but it does not exist yet.

The lecture explains regularization, a method to reduce overfitting by adding a penalty to the cost (loss) function that discourages overly complex models. Overfitting is when a model memorizes noise in the training data and fails to generalize. Regularization keeps model parameters (weights) from growing too large, which helps models generalize better to new data.

This lecture kicks off an Introduction to Machine Learning course by explaining how the class will run and what you will learn. The instructor, Byron Wallace, introduces the TAs (Max and Zohair), office hours, and where to find all materials (Piazza/Canvas). The course has weekly graded homework, a midterm, and a group final project with a proposal, report, and presentation. Lectures are mostly theory and recorded; hands-on coding happens in homework.

Decision trees are flowchart-like models used to predict a class (like yes/no) by asking a series of questions about features. You start at the root and follow branches based on answers until you reach a leaf with a class label. Each internal node tests one attribute, each branch is an outcome of that test, and each leaf gives the prediction.