This is a Machine Learning B course project from DIKU (Department of Computer Science), University of Copenhagen, Spring 2025.
The project contains 7 homework assignments covering core ML topics:
- HA1: Concentration inequalities, bounds theory, Occam's razor
- HA2: Convex optimization fundamentals (cones, epigraphs)
- HA3: SVMs and logistic regression with MNIST dataset
- HA4: Overbooking optimization problem
- HA5: Statistical learning theory and confidence intervals
- HA6: Ensemble methods (AdaBoost) with landcover classification
- HA7: Gradient boosting (XGBoost) with quasars dataset
Additional theory exploration includes conjugate functions, duality theory, and quasi-convexity visualization tools. Each assignment has LaTeX reports, Python implementations, and generated figures.