Course info

This course will study the theory and application of Data Mining, including discussing the steps to build a Data Mining application using the Cross Industry Standard for Data Mining (CRISP-DM) framework, Data Mining Models including Estimation, Forecasting, Association, Clustering and Classification, as well as Methods. Evaluation includes K-Fold Crossvalidation, Hold-Out and Leave One Out Crossvalidation (LOOC). The Data Mining model algorithms discussed in this lecture are Decision Tree, Naive Bayes, K-Nearest Neighbor, Neural Networks, Linear Regression, Logistic Regression, Association Rule, K-Means and Hierarchical Clustering. This lecture also discusses the latest research on Data Mining in the field of E-Health.