Foundation of Data Analytics
This course gives an introduction to computational data analytics, with emphasis on its mathematical foundations. The goal is to carefully develop and explore mathematical methods that build up the backbone of modern data analysis, such as machine learning, data mining and artificial intelligence. Topics include: Bayes rule and connection to inference, linear regression and its polynomial and high dimensional extensions, principal component analysis and dimensionality reduction, classification, clustering, deep neural network as well as dictionary learning and basis pursuit. Students taking this course are expected to have knowledge in basic linear algebra.