Machine learning is the interdisciplinary field at the intersection of statistics and computer science which develops such statistical models and interweaves them with computer algorithms. This course provides an introduction to the theory with a basis in real-world application, focusing on statistical and computational aspects of data analysis. It is designed to serve as an introduction to the fundamental concepts, techniques and algorithms of machine learning. The course will cover following topics: data representation, feature extraction, dimension reduction, supervised and unsupervised classification, support vector machines, latent variable models and clustering, and model selection. During the course of discussion, a main thread of probabilistic models will be used to integrate different statistical learning and inference techniques, including MLE, Bayesian parameter estimation, information-theory-based learning, EM algorithm, and variational methods.
BMTRY 701, 706, knowledge of R