Foundations of Machine Learning

Distinguish between supervised, unsupervised, and reinforcement learning.

Split a dataset into training, validation, and test sets for model evaluation.

Handle missing values and apply feature scaling (normalization, standardization).

Describe the mathematical formulation of a simple linear regression model.

Apply gradient descent to optimize the cost function of a linear regression model.

Implement the k-Nearest Neighbors (k-NN) algorithm for classification and regression.

Implement logistic regression for binary classification tasks.

Implement a decision tree for classification using Gini impurity or information gain.