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Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 3-Day Livestream Seminar Taught by Bruce Desmarais, Ph.D.

The rapidly growing relevance of Machine Learning cuts across scientific disciplines in the humanities, social sciences, and natural sciences. It is increasingly used in research for predictive, explanatory, and exploratory purposes. In 2022 alone, Google Scholar found approximately 278,000 scientific publications that included the phrase “machine learning.”

This course provides a comprehensive introduction to machine learning. Topics include: cross-validation, model evaluation, variable selection, classification, prediction, and regression.

Scientific research is increasingly conducted using data sets that are larger and more complex than the data for which conventional statistical tools were designed. Examples of such data include population-scale information on individual-level consumer and political behavior, data streams collected from social media and other digital sources, and data streamed from physical and environmental sensors.

There are three fundamental ways in which fine-grained, voluminous, and high-dimensional data require a set of methods that are more flexible than the conventional statistical toolkit. First, the data are inherently more complex, making it difficult to specify an adequate statistical model from theory alone. Second, the data are high dimensional, meaning there are more variables than one can include in conventional statistical models. Third, the data contain adequate information to make highly accurate, and importantly, actionable, predictions about unseen data (e.g., forecasts). These three features require an analytical toolkit that is capable of learning model structure, selecting variables, and producing accurate predictions, which are all capabilities of foundational machine learning methods. In this seminar, we will cover foundational machine learning, with a focus on essential concepts and practical application.

The seminar begins by introducing the foundational concepts of predictive modeling and model evaluation with held-out data and cross-validation. Next, we will focus on algorithms for model building and variable selection within the machine learning framework. Finally, we will cover core classes of machine learning models, sometimes referred to as “learners”. These include k-nearest neighbor methods, support vector machines, regression trees, random forests, and XGBoost. Throughout the seminar, participants will gain experience through hands-on exercises.