Prerequisites: Programming (CASCS112 or equivalent), Linear Algebra (CASCS132 or equivalent), Probability (CASCS237 or equivalent), and single-variable calculus (MA 123-124 or equivalent); multi-variable calculus (MA 225 or equivalent) is highly recommended. Introduction to modern machine learning concepts, techniques, and algorithms. Topics include regression, kernels, support vector machines, feature selection, boosting, clustering, hidden Markov models, and Bayesian networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets.