The course covers selective predictive modeling techniques in the fields of statistics, artificial intelligence and machine learning. These include logistic regression (Logit), artificial neural network (ANN), and decision trees (e.g., CHAID, C&RT, C5.0 and QUEST). Assessment methods to evaluate and compare prediction models will also be discussed (e.g., accuracy rates, hit rates, response charts and lift charts).The course will look at developing and deploying predictive modeling applications. Data-mining software will be used intensively in the course, both for solving problems and for enhancing students’ understanding of the theoretical aspects of the course.
Credit Units: 5
Presentation Pattern: Every January
E-Learning: BLENDED - Learning is done MAINLY online using interactive study materials in Canvas. Students receive guidance and support from online instructors via discussion forums and emails. This is supplemented with SOME face-to-face sessions. If the course has an exam component, this will be administered on-campus.