CS 229 - Unsupervised Learning Cheatsheet

Motivation The goal of unsupervised learning is to find hidden patterns in unlabeled data $\{x {(1)},...,x {(m)}\}$. Jensen's inequality Let $f$ be a convex function and $X$ a random variable. Latent variables Latent variables are hidden/unobserved variables that make estimation problems difficult, and are often denoted $z$. We note $c {(i)}$ the cluster of data point $i$ and $\mu_j$ the center of cluster $j$. Algorithm After randomly initializing the cluster centroids $\mu_1,\mu_2,...,\mu_k\in\mathbb{R} n$, the $k$-means algorithm repeats the following step until convergence: Algorithm It is a clustering algorithm with an agglomerative hierarchical approach that build nested clusters in a successive manner. In an unsupervised learning setting, it is often hard to assess the performance of a model since we don't have the ground truth labels as was the case in the supervised learning setting.