# Denetimsiz Öğrenme

Canonical: https://manyspace.aporia.systems/tr/subjects/ai/unsupervised-learning-91
Updated: 2026-10-13

Denetimsiz Öğrenme: yaklaşık 13 saat. Her konu önce neye ihtiyaç olduğunu ve neyi açtığını listeler.

## Clustering

- [Clustering Framing](https://manyspace.aporia.systems/tr/topics/clustering-framing-aiml-uns-001)
- [k-Means](https://manyspace.aporia.systems/tr/topics/k-means-aiml-uns-002)
- [Mixture Models Intuition](https://manyspace.aporia.systems/tr/topics/mixture-models-intuition-aiml-uns-003)
- [Hierarchical Clustering](https://manyspace.aporia.systems/tr/topics/hierarchical-clustering-aiml-uns-008)
- [Density-Based Clustering](https://manyspace.aporia.systems/tr/topics/density-based-clustering-aiml-uns-009)
- [Gaussian Mixture Models](https://manyspace.aporia.systems/tr/topics/gaussian-mixture-models-aiml-uns-010)

## Reduction

- [Dimensionality Reduction Framing](https://manyspace.aporia.systems/tr/topics/dimensionality-reduction-framing-aiml-uns-004)
- [Principal Component Analysis](https://manyspace.aporia.systems/tr/topics/principal-component-analysis-aiml-uns-005)
- [Latent Representations](https://manyspace.aporia.systems/tr/topics/latent-representations-aiml-uns-006)
- [Matrix Factorization and the SVD](https://manyspace.aporia.systems/tr/topics/matrix-factorization-and-the-svd-aiml-uns-012)
- [Probabilistic PCA and Factor Analysis](https://manyspace.aporia.systems/tr/topics/probabilistic-pca-and-factor-analysis-aiml-uns-013)
- [Manifold Learning and Nonlinear Embeddings](https://manyspace.aporia.systems/tr/topics/manifold-learning-and-nonlinear-embeddings-aiml-uns-011)

## Anomalies

- [Anomaly Detection](https://manyspace.aporia.systems/tr/topics/anomaly-detection-aiml-uns-007)