Unsupervised Learning discovers hidden patterns, structures, and representations in unlabeled data without explicit supervision. Key techniques include clustering, dimensionality reduction, density estimation, anomaly detection, and generative modelling, enabling exploratory data analysis and latent feature extraction.
Semantic Classification
Content
Key Characteristics
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Learns from data without labeled examples
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Discovers intrinsic data structure and patterns
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Reduces dimensionality while preserving information
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Identifies outliers and anomalies
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Enables self-supervised and contrastive learning
Overview
Unsupervised Learning discovers hidden patterns, structures, and representations in unlabeled data without explicit supervision. Key techniques include clustering (k-means, hierarchical, DBSCAN), dimensionality reduction (PCA, t-SNE, UMAP), density estimation, anomaly detection, and generative modeling (VAEs, GANs). Unsupervised methods extract meaningful features, identify data clusters, reduce dimensionality for visualization, and learn latent representations. Applications span exploratory data analysis, data preprocessing for supervised learning, recommendation systems, and generative AI.
Related Concepts
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References
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Hastie, T. et al. (2009). The Elements of Statistical Learning (2nd ed.). Springer.
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Kingma, D. & Welling, M. (2014). Auto-Encoding Variational Bayes. ICLR 2014.
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Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS 2014.