Advanced Machine Learning - Online Course
A 4-Day Livestream Seminar Taught by
Bruce DesmaraisTuesday, July 29 –
Friday, August 1, 2025
10:30am-12:30pm (convert to your local time)
1:30pm-3:00pm
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.
Starting July 29, we are offering this seminar as a 4-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
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.
More details about the course content
Through this advanced seminar, you will learn how to:
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- Manage incomplete data and the biases it can introduce.
- Uncover causal relationships and treatment effects using double machine learning.
- Work with large language models via transformer-based architectures.
- 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.
Blog Posts
Machine learning isn’t a rejection of statistics—it’s a modern extension of it. In The Statistical Foundations of Machine Learning, Professor Desmarais illustrates how the two fields are more deeply connected than you might realize, rooted in regression and probabilistic thinking.
Read previous blog posts here:
Through this advanced seminar, you will learn how to:
-
- Manage incomplete data and the biases it can introduce.
- Uncover causal relationships and treatment effects using double machine learning.
- Work with large language models via transformer-based architectures.
- 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.
Blog Posts
Machine learning isn’t a rejection of statistics—it’s a modern extension of it. In The Statistical Foundations of Machine Learning, Professor Desmarais illustrates how the two fields are more deeply connected than you might realize, rooted in regression and probabilistic thinking.
Read previous blog posts here:
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: Managing Missing Data in Machine Learning
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- Missing data mechanisms (MCAR, MAR, MNAR)
- Single vs. multiple imputation
Day 2: Causal Inference and Double Machine Learning
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- Introduction to causal inference (potential outcomes, identification strategies)
- Machine learning for causal effect estimation
- Double machine learning (DML) theory and implementation
Day 3: Working with Transformers and Large Language Models
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- Overview of deep learning for NLP (transformer architecture)
- Pre-training, fine-tuning, and domain adaptation
Day 4: Active Learning for Efficient ML Training
-
- Key concepts of active learning
- Implementing an active learning loop in practice
Day 1: Managing Missing Data in Machine Learning
-
- Missing data mechanisms (MCAR, MAR, MNAR)
- Single vs. multiple imputation
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
Day 3: Working with Transformers and Large Language Models
-
- Overview of deep learning for NLP (transformer architecture)
- Pre-training, fine-tuning, and domain adaptation
Day 4: Active Learning for Efficient ML Training
-
- Key concepts of active learning
- Implementing an active learning loop in practice
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.