8 docs tagged with "unsupervised-learning"

Neural network-based dimensionality reduction: Encoder-Decoder architecture and bottleneck representations.

Discovering clusters of arbitrary shapes and identifying outliers using density-based spatial clustering.

Reducing feature complexity while preserving information: Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

Probabilistic clustering using Expectation-Maximization and the Normal distribution.

Understanding Agglomerative clustering, Dendrograms, and linkage criteria.

Grouping data into K clusters by minimizing within-cluster variance.

Mastering feature extraction, variance preservation, and the math behind Eigenvalues and Eigenvectors.

Discovering patterns in unlabeled data through clustering, association, and dimensionality reduction.