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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.

Machine learning is now central to research and industry practice—but real-world problems rarely fit neatly into textbook frameworks. Data are incomplete, causal questions matter, labels are scarce, and computational constraints are real. At the same time, generative AI has moved from novelty to practical tool, raising new questions about when it genuinely improves analytical workflows and how to validate its outputs responsibly.

While regression, classic learners, and standard variable selection remain foundational, they are often insufficient for these modern challenges. Practitioners need principled strategies that extend beyond conventional pipelines while preserving rigor and interpretability.

In this advanced seminar, we focus on four applied strategies for navigating these complexities effectively:

• • Maintaining analytical integrity in complex data settings, including principled use of generative methods for imputation and synthetic data generation.
  • Rigorous ML-assisted approaches to causal inference, clarifying where machine learning strengthens identification and where it does not.
  • Integrating generative AI with conventional ML, identifying when generative models offer genuine advantages (e.g., data annotation), and outlining appropriate validation practices for AI-generated outputs.
  • Active learning under real-world constraints, optimizing model performance when labeled data are limited, including LLM-assisted labeling with structured human oversight.

You will leave with a clearer framework for deciding when to use traditional ML, when to incorporate generative AI, and how to combine them in defensible, high-quality analytical workflows.