Event Phone: 1-610-715-0115

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.

An 8-Hour Livestream Seminar Taught by Ross Jacobucci, Ph.D.

This seminar is one-part of a two-part sequence on Advanced Machine Learning methods. While this seminar aims to provide you with the core knowledge needed to apply and evaluate advanced algorithms, Applied Deep Learning using Python focuses on applying deep learning algorithms to text and image data and insight into how deep learning methodologies can shape the design of studies. Register for one or both.

Machine learning (i.e., artificial intelligence, big data, supervised learning, data science) has enormously impacted academic research and industry. Machine learning algorithms have transformed the analysis of extensive datasets, empowering us to uncover deeper insights and make more informed decisions by leveraging advanced data processing and pattern recognition capabilities.

While machine learning has become more widely available and easier to use, it presents several challenges, including preventing overfitting, interpreting the results it produces, and dealing with the inevitable issues that arise from analyzing diverse data types. At its core, most machine learning applications involve applying complex algorithms to predict a single outcome, with more advanced applications often only requiring slight modifications. This course aims to provide each participant with the core knowledge needed to apply and evaluate advanced algorithms.

The course focuses on traditional tabular data (i.e., row = people; columns = variables). It assumes participants are familiar with regularization in regression, cross-validation, and decision trees. Course content includes predicting single outcomes (supervised learning) and tree-based ensemble methods (random forests; boosting). Concepts will be paired with hands-on exercises showing how to apply, assess, and interpret these methods.

The course will closely follow Chapters 3-8 of the author’s co-authored book Machine Learning for Social and Behavioral Research (Jacobucci, Grimm, & Zhang, 2023). This book is not required, but can serve as a reference for those wishing additional information.