Data Matrices and Dimensional Reduction

Data Matrices and Dimensional Reduction#

About the project#

  • Duration: 3-4 hours in class, 1-2 hours preparation at home

  • Prerequisites: Linear algebra (matrices, eigenvalues, eigenvectors, orthogonal projections), basic statistics concepts, basic Python programming

  • Python packages: NumPy, matplotlib, scipy (optional: pandas for data handling)

  • Learning objectives: Understand Principal Component Analysis (PCA), work with data matrices in NumPy, perform dimensional reduction, visualize high-dimensional data, apply eigenvalue decomposition to real datasets

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