This chapter introduces unsupervised learning, where algorithms analyze data without predefined labels or target outcomes. It covers three main clustering approaches: agglomerative clustering (bottom-up approach merging similar data points) and divisive clustering (top-down approach, exemplified by k-means algorithm that partitions data into k groups by minimizing distances to centroids).

The chapter explains Expectation Maximization (EM) algorithm for handling incomplete data and finding maximum likelihood parameters in statistical models. It includes a section on reinforcement learning, where agents learn optimal actions through trial-and-error interactions with environments to maximize rewards.

Key topics include distance matrices, dendrograms, cluster evaluation metrics (AIC, BIC), and practical applications. The chapter emphasizes the artistic nature of unsupervised learning, requiring careful design decisions about thresholds, cluster numbers, and technique selection. Hands-on R examples demonstrate each method using real datasets.

This chapter covers unsupervised learning, where algorithms analyze data without known true labels or outcomes. Unlike supervised learning, the goal is to discover hidden patterns and structures in data.

The chapter explores three main techniques: Agglomerative clustering works bottom-up, starting with individual data points and merging similar ones into larger clusters. Divisive clustering (including k-means) takes a top-down approach, splitting data into smaller groups. Both methods use distance matrices and dendrograms to visualize cluster relationships.

Expectation Maximization (EM) handles incomplete data by iteratively estimating missing parameters using maximum likelihood estimation. Model quality is assessed using AIC and BIC criteria.

The chapter also introduces reinforcement learning, where agents learn optimal actions through trial-and-error interactions with environments, receiving rewards or penalties. Applications include robotics, gaming, and autonomous systems. Throughout, the chapter emphasizes the creative, interpretive nature of unsupervised learning compared to more structured supervised approaches.