Do Causality like a Bayesian

The Bayesian mental model for machine learning in practice

You probably already know about and have applied Bayes rule, or you have at least heard of it. In this course, you will go beyond Baye's rule to acquiring a Bayesian mental model for tackling machine learning problems, and building learning agents that drive decision-making in organizations.

Relationship to other modules

However, if you were to stop after completing this module, you would walk away with solid experience in causal modeling using deep generative machine learning.

Your Instructor

Robert Osazuwa Ness
Robert Osazuwa Ness

Robert didn't start out working on machine learning. He initially pursued a career in developmental economics. Robert became fluent in Mandarin Chinese and moved to Tibet to work with an economic development organization. He later obtained a graduate degree from Johns Hopkins School of Advanced International Studies.

Robert's interests later shifted towards software engineering. While working for tech companies in China, he developed an interest in modeling data. These interests lead to his pursuit of a Ph.D. in statistics at Purdue University.

Robert's research focuses on causal inference, probabilistic modeling, sequential decision processes, and dynamic models of complex systems. He has published in journals and venues across these spaces, including RECOMB and NeurIPS.

He works as a research engineer for the AI startup based in Boston, and is a machine learning professor at Northeastern University.

Frequently Asked Questions

When does the course start and finish?
The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.
What if I am unhappy with the course?
We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.
What is the technical background required for this course?
This course is provides a high-level overview. Deeper mathematical explanations and code examples are included as supplementary notes for students who want to go deeper. When mathematical notation and code make it into the lecture, it is meant to be easy to understand even if the ideas are entirely new to the student. However, the natural language explanations will provide useful insights for anyone who has never seen mathematical notation.
I am a manager/investor after a high-level overview that can inform stategic decisions. Is this for me?
Yes. You'll find lots of material on the math of Bayesian analysis. In contrast, this is a course on Bayesian-themed mental models for model-building and decision-making.
I already know all about Bayesian statistics. Will this course benefit me?
Very probably yes. The course is connects causal modeling to these ideas, which is not a connection that people commonly make. The goal of the course is to give you a unique way of thinking about problems, rather than teach you math. You don't have to take our word for it. Try it, and get a refund if it doesn't work for you.
Where's the causal inference? This doesn't look like causal inference...
The goal of this course is to connect causal modeling to machine learning in a practical way. Specific causal inference topics such as causal effect estimation, confounder adjustment, propensity scores, instrumental variables, potential outcomes, etc. are covered in other AltDeep courses.

This course is closed for enrollment.