Skip to content
All tags

#pca

2 posts
ai deep-dive Reading Stanford CS229

Principal Components Analysis: Projection, Reconstruction, and Reduction

Chapter 12 formulates PCA as geometric optimization: maximize projected variance along a unit direction to obtain the leading eigenvector of the covariance matrix. The top k eigenvectors give both maximum retained variance and minimum linear reconstruction error.

#cs229#machine-learning#unsupervised-learning#pca#dimensionality-reduction
learning guide Reading CMU 10-301 Machine Learning

CMU 10-301 HW9: Close the Course with Ensembles, k-Means, PCA, and Recommenders

The final written assignment combines ensembles, clustering, representation, and recommendation to test whether you can choose a learning paradigm from problem structure.

#cmu#machine-learning#clustering#pca#recommender-systems