Machine Learning
Learning from data: the classical ML toolkit.
Foundations of Machine Learning
Machine learning teaches computers to improve from experience — turning raw data into generalizing functions without being explicitly programmed for every case.
Linear & Logistic Regression
From scatter plots to decision boundaries: the geometric and probabilistic foundations that underpin nearly every supervised learning system ever built.
Bias-Variance & Regularization
Master the fundamental tension in all of machine learning: models that memorize versus models that generalize, and the elegant mathematical tools that navigate between them.
Support Vector Machines & Kernels
SVMs find the widest possible street between classes, then use the kernel trick to bend that street through curved, high-dimensional space — giving you a provably optimal linear classifier that secretly solves nonlinear problems.
Decision Trees & Ensembles
From a single question at the root to a forest of millions of trees — decision trees and ensembles transform greedy splits and Shannon entropy into the most practical and powerful models in modern machine learning.
Unsupervised Learning
Unsupervised learning discovers hidden structure in unlabeled data — clustering, density estimation, and dimensionality reduction are its core instruments.
Neural Networks & Backpropagation
Neural networks learn by composing simple units into deep hierarchies, then flowing error signals backward through the chain rule to sculpt every weight toward better predictions.
Evaluation & ML in Practice
Learn to measure what actually matters: the art and science of honestly evaluating whether your model is learning or just memorizing.