GEO6200 - Spatial Statistics and Disease Mapping

Course Description

Statistical methods for disease data that include geographic information.  Methods include spatial scan statistics, kriging, measures of autocorrelation, Moran's I, regression with exposure data and covariates.  Disease maps and relative risk estimation.  Mapping and geographic information systems.  Bayesian methods of estimation for conditional autoregressive models.

Course Outline

• What is Machine Learning?
• Regression
• Nearest Neighbors
• Support Vector Machines
• Probabilistic Models
• Ensemble Methods
• Clustering Algorithms
• Neural Networks and Deep Learning
• Decision Trees and Random Forests

Learner Outcomes

• Select a machine learning model and algorithm appropriate for a given problem;
• Formulate an appropriate evaluation scheme in order to tune model parameters and measure the quality of results;
• Apply machine learning techniques to real data sets to solve a problem in a chosen domain, and interpret the results.

Duration