Computation is the key to machine learning and data science. This course covers the fundamental techniques and theory needed for implementing algorithms and analyzing data.
Topics include: unsupervised learning (clustering, dimension reduction, density estimation), supervised learning (regression, convex optimization, kernel methods), and more advanced topics in machine learning (Markov models, reinforcement learning, etc.).