🎬AI Lectures17

Decision trees are models that make predictions by asking a series of yes/no questions about features, like a flowchart. You start at a root question, follow branches based on answers, and end at a leaf that gives the prediction. This simple structure makes them easy to read and explain to anyone.

Logistic regression is a simple method for binary classification that outputs a probability between 0 and 1 for class 1. It takes a weighted sum of input features (w^T x + b) and passes it through the sigmoid function. The sigmoid is an S-shaped curve that squashes any real number into the [0,1] range.

Machine learning lets computers learn patterns from data instead of following hand-made rules. Instead of writing instructions like “pointy ears = cat,” we feed many labeled examples and let the computer discover what features matter. This makes ML flexible and powerful for messy, real-world problems where rules are hard to write. Arthur Samuel’s classic definition captures this: computers learn without being explicitly programmed.

This lecture explains L1 regularization, also called LASSO, as a way to prevent overfitting by adding a penalty to the loss that depends on the absolute values of model weights. Overfitting means a model memorizes the training data but fails on new data. By penalizing large weights, L1 helps the model focus on the strongest, most useful features.

Decision trees are simple, powerful models that make predictions by asking a sequence of yes/no questions about input features. They work for both classification (like spam vs. not spam) and regression (like house price prediction). The tree’s structure—what to split on and when—comes directly from the data, which is why we call them non-parametric.

Kernel methods turn simple linear algorithms into powerful non-linear ones. Instead of drawing only straight lines to separate data, they let us curve and bend the boundary by working in a higher-dimensional feature space. This keeps training simple while unlocking complex patterns.

Principal Component Analysis (PCA) is a method to turn high-dimensional data into a smaller set of numbers while keeping as much useful information as possible. The lecture explains three equivalent views of PCA: best low-dimensional representation, directions of maximum variance, and best reconstruction after projection. These views all lead to the same solution using eigenvectors and eigenvalues of a certain matrix built from the data.

This lecture teaches linear regression as a simple but powerful way to predict a number (output) from input features. The model assumes a straight-line relationship between inputs and output, written as y = W^T x. Each weight in W shows how much a feature pushes the prediction up or down.

This lecture explains how to choose and fairly evaluate machine learning models so they work well on new, unseen data. The main goal is generalization, which means the model should not only do well on the training set but also on future examples. Overfitting (too complex) and underfitting (too simple) are the two big mistakes to avoid.

Decision trees are models that turn feature values into decisions like Yes/No. You follow a path from the root question down through branches until you reach a leaf decision. They are great for problems where the target is discrete, like classifying outcomes. They also tolerate noisy data and can work even when some feature values are missing.