🎬AI Lectures17

This lecture introduces decision trees, a classic machine learning method that makes choices by asking a series of yes/no or small set-of-value questions. Each internal node is a test on a feature (like 'Is it Friday or Saturday?'), each branch is the outcome, and each leaf is a predicted label. You classify by starting at the root, following the test outcomes, and stopping at a leaf.

Machine learning is about computers learning patterns from data instead of being told each rule by a programmer. Rather than hardcoding how to spot a cat in a photo, you show many labeled cat and non-cat images, and the computer learns what features matter. This approach shines when problems are too complex to describe with step-by-step rules.

This lesson explains the core pieces of machine learning: data (X and Y), models f(x;θ), loss functions that measure mistakes, and optimizers that adjust θ to reduce the loss. It divides learning into supervised (with labels), unsupervised (without labels), and reinforcement learning (with rewards). The focus here is on supervised learning, especially regression and classification, plus a short intro to k-means clustering.