Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration two weeks or more before the course is scheduled to begin, you are entitled to receive your choice of either a credit for a future seminar (which can be applied toward any of our courses) or a refund of the registration fee (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 role of advanced machine learning techniques in both academic and industry settings is rapidly expanding, driven by increasingly complex analytical demands and applied workflows that go beyond traditional predictive modeling. Foundational methods, such as basic regression analysis, classic learners, and standard approaches to variable selection, remain essential tools but often fall short in addressing the challenges that arise in real-world data analysis. Practitioners frequently encounter complexities like missing data, questions regarding causal interpretation, and distinguishing between the capabilities of emerging generative AI technologies and traditional machine learning methods. Additionally, practical limitations, such as constrained computational resources or data availability, further complicate straightforward applications of standard machine learning workflows.

In response to these challenges, this advanced seminar introduces a set of sophisticated strategies designed to navigate these complexities effectively. We focus on four key areas that extend beyond conventional machine learning frameworks, equipping you with advanced methodological tools tailored to complex scenarios. These areas address how to systematically manage missing data to preserve analytical integrity, apply rigorous frameworks for causal inference to ensure accurate interpretation, understand and strategically leverage generative AI in contrast to conventional ML approaches, and optimize model performance within real-world constraints where labeling resources are limited. This comprehensive exploration aims to enhance your ability to implement state-of-the-art solutions across diverse applied machine learning contexts.

Through this advanced seminar, you will learn how to:

1. 1. Manage incomplete data and the biases it can introduce.
   2. Uncover causal relationships and treatment effects using double machine learning.
   3. Work with large language models via transformer-based architectures.
   4. Employ active learning to streamline both measurement and model training.

By combining engaging lectures with practical exercises, this workshop will build your skills in modern machine learning methods, equipping you to tackle complex data problems in research and applied settings.