Skip to main content
grasp.study
About
Explore
Pricing
Log in
Try it for free
Back to course
👋
Welcome to a Grasp lesson
Create a free account
to keep studying and create your own study path.
Introduction to Data Science (DS 215)
·
Module 11
Dimensionality Reduction with PCA
1
PCA: Maximizing Variance with Lagrange Multipliers
Derive Principal Component Analysis (PCA) from first principles by maximizing variance using Lagrange Multipliers.
2
PCA: Rotating for Maximal Variance
Explain the geometric interpretation of PCA as a rotation of the coordinate system to align with axes of maximal variance.
3
PCA and Covariance Matrix Eigendecomposition
Relate the PCA solution to the eigendecomposition of the covariance matrix.
4
PCA with NumPy's SVD
Implement PCA from scratch using NumPy's SVD function.
5
PCA for Face Recognition and Visualization
Project: Apply PCA to a high-dimensional dataset (e.g., images of faces) and visualize the principal components.
Previous module
Classification, Clustering, and Mixture Models
Next module
Introduction to Neural Networks