Supervised and Unsupervised Methods

Catalog description

This course provides a comprehensive introduction to predictive modeling, using both traditional biostatistics approached and modern machine learning techniques. Topics include tools and techniques for data processing, general rules and techniques for measuring model performance, linear regression and its related methods, non-linear models such as support vector machine and neural network, trees and rule-based methods, and so on. Both supervised and unsupervised learning methods are described, and methods for both continuous and categorical data will be discussed. The course is focused on the skills and tools for solving practical problems.