The result of the Gaussian SVM can be read as a linear vote over Gaussians centered at the support vectors; let both the centers and the votes be learned and you have the RBF network. When all N examples serve as centers, the optimal votes for regression are β = Z⁻¹y and the training error is exactly 0, so you regularize with ridge or by cutting the centers down to M ≪ N prototypes. The prototypes are found with k-means, which alternates two steps, assigning every example to its closest center and moving every center to the average of its cluster, until the clusters stop changing.
Lectures 4–7 of CS189 Spring 2026 tie unsupervised learning into one thread. K-means clusters the data, then its weaknesses show up: hard assignments, no probabilistic framing, and a bias toward round clusters of similar size. The course reviews probability, introduces maximum likelihood estimation (MLE) and multivariate Gaussians, and rewrites K-means as a Gaussian mixture model (GMM). The GMM log-likelihood has no closed-form solution, and that gap leads the course to gradient descent. All four lectures have slides and video; Discussions 2–3 come with solutions and walkthroughs.
HW5 Problems 3–4 give handwritten digits to three methods that never see a label: K-means summarizes the data with 10 mean images, HAC builds a merge tree you can cut at any number of clusters, and PCA compresses images onto a few continuous directions. All three answer the same question — how much error do you pay to describe the data with a few objects — and the assignment makes you compare their objectives and reconstruction errors directly.
The final written assignment combines ensembles, clustering, representation, and recommendation to test whether you can choose a learning paradigm from problem structure.