Course Objectives
- The aim of this course is show to graduate students how to perform data mining, prediction and
data analysis in the manufacturing and service sectors. - Moreover, the second aim is to make students to gain practice about applying these techniques to
real life problems
Topics
- Fundamentals of Data Mining
- Overview of data-driven modeling and review of basic statistics
- Principal Component Analysis
- Linear regression and related topics
- Classification (Logistic Regression and Discriminant Analysis)
- Model validation through cross validation and bootstrapping
- Regression with nonlinear shapes
- Tree and ensemble learning
- Support Vector Machine
- Clustering
- Anomaly detection
