Theory and Methods for the Analysis of Social Networks
The rapid growth of digitalized data and the computer power available to analyze it has created immense opportunities for both machine learning and data mining. This course introduces machine learning and data mining methods. Topics covered include information retrieval, clustering, classification, modern regression, cross validation, boosting and bagging. Course emphasizes selection of appropriate methods and justification of choice, use of programming for implementation of the method, and evaluation and effective communication of results in data analysis reports. Pre-requisites: STA 210 and STA 230. Co-requisite: STA 250. Suggested background: STA 323.