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Unsupervised learning

Finding structure when there is no answer key, and knowing when the structure is in the data or in the method.

  1. Structure Discovery Without Answer Keys
  2. Units, Features, and Preprocessing for Unsupervised Data
  3. Distance, Similarity, and Neighborhoods
  4. High-Dimensional Geometry and Mixed Data
  5. Similarity Graphs, Density, and Connectivity
  6. Evaluating Unsupervised Structure Without Labels
  7. K-Means: Objective and Lloyd’s Algorithm
  8. K-Means Initialization, K Selection, and Failure Modes
  9. Centroid Alternatives and Large-Scale Partitioning
  10. Hierarchical Clustering and Dendrograms
  11. Linkage Choices, Connectivity, and Tree Cuts
  12. DBSCAN: Core, Border, and Noise
  13. OPTICS and HDBSCAN Across Density Scales
  14. Gaussian Mixtures, EM, and Soft Membership
  15. Spectral Clustering and Graph Cuts
  16. BIRCH, Streaming Summaries, and Massive Data
  17. Categorical and Mixed-Type Clustering
  18. Constrained and Semi-Supervised Clustering
  19. Biclustering and Co-Clustering
  20. Text and Embedding Clustering
  21. Time-Series Clustering and Alignment
  22. Spatial and Geospatial Clustering
  23. Graph Community Detection and Network Partitions
  24. Interpreting, Naming, and Profiling Clusters
  25. Stability, Consensus, and Reproducibility
  26. Production Assignment, Drift, and the Cluster Lifecycle
  27. Anomaly Detection: Scope, Reference, and Rarity
  28. Local Outlier Factor and Neighborhood Anomalies
  29. Isolation Forest and One-Class Boundaries
  30. Time-Series Anomalies and Change Points
  31. Anomaly Thresholds, Alert Budgets, and Evaluation
  32. PCA: Variance, Components, and SVD Intuition
  33. PCA in Practice: Scaling, Whitening, and Reconstruction
  34. NMF, ICA, and Interpretable Factorizations
  35. Random Projections, Feature Agglomeration, and Compression
  36. Manifold Learning: MDS, Isomap, LLE, and Diffusion Maps
  37. t-SNE: Local Neighborhood Visualization
  38. UMAP: Graph-Based Embeddings and Tradeoffs
  39. Projection Traps, Trustworthiness, and Joint Workflows
  40. Capstone: Build and Defend an Unsupervised System