This lecture continues to discuss optimization in the context of the knapsack problem, and talks about the difference between greedy approaches and optimal approaches. It then moves on to discuss supervised and unsupervised machine learning optimization problems. Most of the time is spent on clustering.

Topics covered: Knapsack problem, local and global optima, supervised and unsupervised machine learning, training error, clustering, linkage, feature vectors.