This episode of Techsplainers explores unsupervised learning, the branch of machine learning where algorithms discover hidden patterns in data without human guidance or labeled examples. The discussion covers the three main tasks of unsupervised learning: clustering (grouping similar data points), association rules (finding relationships between variables), and dimensionality reduction (simplifying data while preserving essential information). We examine popular algorithms like K-means clustering, hierarchical clustering, the Apriori algorithm for market basket analysis, and techniques like Principal Component Analysis and autoencoders. The episode highlights real-world applications including news aggregation, recommendation engines, medical imaging, and customer segmentation. The conversation also compares unsupervised learning with supervised approaches and addresses challenges such as computational complexity, validation difficulties, and interpretation of results, offering listeners a comprehensive understanding of how AI can extract valuable insights from unlabeled data.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Anna Gutowska