Machine Learning

Foundational machine learning algorithms through math-first learning.

Regression

Linear Regression

Fit a line to data by minimizing mean squared error using gradient descent.

Classification

k-Nearest Neighbors

Classify a point by majority vote of its k closest neighbors in feature space.

Clustering

K-Means Clustering

Partition data into k clusters by iteratively assigning points and updating centroids.

Classification

Logistic Regression

Binary classification using the sigmoid function and cross-entropy loss.

Classification

Decision Tree

Recursively split feature space using information gain to build interpretable classifiers.

Classification

Support Vector Machine

Find the maximum-margin hyperplane that separates two classes, with support vectors defining the boundary.

Dimensionality Reduction

Principal Component Analysis

Reduce dimensionality by projecting data onto the directions of maximum variance.

Ensemble

Random Forest

Ensemble of decision trees trained on bootstrapped data with feature subsampling.

Deep Learning

Neural Network

Multi-layer perceptron that learns nonlinear decision boundaries via backpropagation.

Classification

Naive Bayes

Probabilistic classifier using Bayes' theorem with the conditional independence assumption.