Machine Learning is a five-day workshop focused on the application and interpretation of both traditional and next-generation machine learning (also statistical learning) approaches. One of the fastest-growing areas of statistics and data analysis, machine learning applications have increased rapidly within the psychological, health, and social sciences. These techniques are applied with the goal of producing robust models for predictions (continuous outcomes) or classifications (categorical outcomes), and focus on achieving high accuracy rather than testing null hypothesis or statistical significance.
This workshop provides participants with the understanding and practical tools to choose between machine learning techniques and use these with confidence. The workshop begins with common, fundamental models that are widely used, such as regression and logistic regression, and uses these to introduce and understand more advanced approaches such as regularization, splines, support vector machines, classification trees, and discriminant analysis. Along the way, a comprehensive approach to assessing model fit and protecting against overfitting will be developed. Finally, this workshop will also delve into the tension between “prediction” and “inference” and explores the implications for common research applications.