Introduction to Machine Learning (CSCI3237)

Running the practical sessions for the undergraduate machine learning course — three credits over fifteen weeks. The syllabus builds the mathematical floor first (vectors and norms, least squares, eigenvalues and SVD, probability and statistics) before regression, classification and clustering, and closes on evaluation metrics, overfitting and interpretability. The practicals are where that becomes something students can run: environment setup and first contact with data, then pandas, then models they implement and break themselves.