Dimensionality Reduction with SVD & PCA

Intermediate

Master SVD and PCA for dimensionality reduction. Learn to compress data while preserving essential information, select optimal component counts, and reconstruct data with controlled information loss.

75 min Lab: notebook 5 objectives 4 evidence types
Open Interactive Lab
75
Minutes
5
Objectives
4
Evidence Types
4
Success Criteria

Case Narrative

Learning Objectives

1
Understand SVD as a fundamental matrix factorization
2
Apply PCA for dimensionality reduction with variance tracking
3
Select optimal number of components using explained variance
4
Analyze reconstruction error and information loss
5
Apply reduction techniques to real-world datasets

Required Evidence

Svd Decomposition Not collected yet
Variance Analysis Not collected yet
Component Selection Not collected yet
Reconstruction Evaluation Not collected yet

Case Details

Difficulty
Intermediate
Duration
75 min
Lab Type
notebook
Slug
dimensionality-reduction

Prerequisites

No prerequisites - open to all

Success Criteria

Pca Applied Required
Reconstruction Quality Measured Required
Svd Computed Required
Variance Threshold Selected Required

Tags