Term tag: unsupervised learning

Anomaly Detection (AD)

A 99%-accurate detector can still hand you a queue that is 99% false alarms. How anomaly detection scores, thresholds and ranks rare events.

Clustering

Grouping unlabeled data into clusters: which algorithm to pick, why each one imposes a shape, how to choose k, and how to tell if the clusters are real.

Dimensionality Reduction (DR)

Why high-dimensional data has to be compressed, how PCA picks its axes, and why cluster sizes and gaps in a t-SNE or UMAP plot mean nothing.

Machine Learning (ML)

How a machine finds a rule nobody wrote: a line fitted to four points by hand, loss falling from 433.5 to 6.38 in three steps, and why 98% accuracy lies.

Principal Component Analysis (PCA)

Principal Component Analysis (PCA) reduces dimensions by projecting data onto orthogonal directions of maximum variance, ranked by the variance each keeps.

Self-supervised Learning (SSL)

Training a model on unlabeled data by hiding part of each input and making the model predict it — the paradigm behind next-token prediction and modern LLMs.

Unsupervised Learning

Finding structure in unlabeled data — no correct answers are given, so the model discovers groupings, patterns, or a compressed representation on its own.