Advanced Machine Learning and Applied AI Workflows - Online Course
A 3-Day Livestream Seminar Taught by
Bruce DesmaraisWednesday, May 13 —
Friday, May 15, 2026
10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
This seminar is part of our Machine Learning Certification, a flexible 4-course pathway designed to build practical expertise in modern machine learning. Contact us to learn how you can complete the certification and access discounted pricing.
NOTE: This course is designed for those who have previous experience with machine learning methods. If you are looking to learn the basics, check out Machine Learning.
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.
Starting May 13, this seminar will be presented as a 3-day synchronous, livestream workshop via Zoom. Each day will feature two lecture sessions with hands-on exercises, separated by a 1-hour break. Live attendance is recommended for the best experience. But if you can’t join in real time, recordings will be available within 24 hours and can be accessed for four weeks after the seminar.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
ECTS Equivalent Points: 1
Computing
This seminar will use R for all the computing tasks. To participate in the hands-on exercises, you are encouraged to use a computer with the most recent version of R and RStudio installed. RStudio is an integrated development environment (IDE) for R that makes a powerful companion to the R programming language. Both R and RStudio are free and available for all major operating systems.
There will be some illustration of Python using the free tier of Google Colab.
To follow the presentation and do the exercises, you should feel comfortable using regression models and advanced methods in R, such as multilevel modeling, basic machine learning, or network analysis.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
This seminar will use R for all the computing tasks. To participate in the hands-on exercises, you are encouraged to use a computer with the most recent version of R and RStudio installed. RStudio is an integrated development environment (IDE) for R that makes a powerful companion to the R programming language. Both R and RStudio are free and available for all major operating systems.
There will be some illustration of Python using the free tier of Google Colab.
To follow the presentation and do the exercises, you should feel comfortable using regression models and advanced methods in R, such as multilevel modeling, basic machine learning, or network analysis.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
Who should register?
If you are interested in advancing your machine learning skills and learning how to deploy machine learning methods more comprehensively and efficiently, this seminar is for you. To get the most out of this seminar, you should have at least some prior training in machine learning.
If you are interested in advancing your machine learning skills and learning how to deploy machine learning methods more comprehensively and efficiently, this seminar is for you. To get the most out of this seminar, you should have at least some prior training in machine learning.
Seminar outline
Day 1: Advanced ML Methods for Missing Data and Data Annotation
-
- Missing data mechanisms (MCAR, MAR, MNAR)
- Multiple Imputation
- Active learning for data annotation
Day 2: Causal Inference and Double Machine Learning
-
- Introduction to causal inference (potential outcomes, identification strategies)
- Machine learning for causal effect estimation
- Double machine learning (DML) theory and implementation
- Interpreting DML-based estimates
Day 3: Working with Transformers and Large Language Models
-
- Overview of deep learning for NLP (transformer architecture)
- Survey of Large Language Models
- Zero-shot learning with LLMs
- Transparency and Explainability
Day 1: Advanced ML Methods for Missing Data and Data Annotation
-
- Missing data mechanisms (MCAR, MAR, MNAR)
- Multiple Imputation
- Active learning for data annotation
Day 2: Causal Inference and Double Machine Learning
-
- Introduction to causal inference (potential outcomes, identification strategies)
- Machine learning for causal effect estimation
- Double machine learning (DML) theory and implementation
- Interpreting DML-based estimates
Day 3: Working with Transformers and Large Language Models
-
- Overview of deep learning for NLP (transformer architecture)
- Survey of Large Language Models
- Zero-shot learning with LLMs
- Transparency and Explainability
Payment information
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.