Unsupervised Learning: Networks and Probabilistic Methods

Unsupervised learning versus supervised learning

Unsupervised learning is a machine learning paradigm where algorithms learn patterns from data without using classification or regression labels. In contrast to supervised learning—where training relies on labeled examples—unsupervised learning typically uses “in the wild” datasets that are cheaper to collect (e.g., large web text corpora) and focuses on discovering structure in the input itself. Unsupervised learning can be viewed through multiple aspects: the data (unlabeled), the training objective (learning to model or reconstruct patterns), the algorithmic approach (e.g., clustering, dimensionality reduction, latent-variable modeling), and the downstream uses (such as feature learning or anomaly detection). After deep learning’s rise, many large-scale unsupervised methods train general neural architectures with gradient descent, using carefully designed training procedures (often generative or reconstruction-based).

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Data, training, algorithm, and downstream aspects

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