Classical Machine Learning: Supervised Learning Foundations

Master the mathematical foundations and practical implementation of classical supervised learning algorithms. Build intuition through interactive visualizations while understanding the theory that powers modern ML.

Learning Objectives

• Understand the mathematical foundations of supervised learning
• Master linear models: regression and classification
• Build intuition for decision trees and their ensembles
• Implement Support Vector Machines from scratch
• Master model evaluation and validation techniques
• Understand bias-variance tradeoff and regularization
• Learn feature engineering and selection strategies
• Apply ML algorithms to real-world problems

Course Content