MSDA 6335 Machine Learning Applications in the Social Sciences
Machine learning has emerged as an indispensable subject in applied data analytics with the goal of creating flexible yet consistent predictive statistical models. This course begins with an overview of the connection between multiple regression analysis and machine learning techniques with a focus on process. Opensource statistics software, namely R, will be used as a vehicle to explore machine learning topics including cross-validation, model selection in machine learning, variable selection, algorithms for multiple regression, machine learning techniques for categorical dependent variables, classification techniques, neural networks, support vector machines, recursive partitioning, ensemble models, and the evaluation of model performance. Special emphasis will be placed on the application of widely used machine learning algorithms versus their construction. How to communicate the results of data analysis projects to firm stakeholders will also be covered.
Offered
Summer, Fall, Spring