This repository contains Jupyter notebook-based lessons for a university graduate course on computational linear algebra. The course focuses on implementing matrix computations with acceptable speed and accuracy, covering topics such as matrix decomposition, dimensionality reduction, and numerical stability. Lessons utilize NumPy, Scikit-Learn, Numba for performance, and PyTorch for GPU acceleration. It serves as foundational mathematical preparation for data science and deep learning practitioners.