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Model-based Thinking in Machine Learning

The unexpected guide to moving beyond common curve-fitting, 10x'ing ML skill development, and becoming an AI creative

Break out of the mentality curve-fitting, overthinking training, and under-thinking how the data is generated.

Deconstruct your biases, and acquire new mental models for applied data science and machine learning.

With this module we perform this mental refactoring. We will graft on a high-level understanding of causality on the a foundation of deep generative machine learning.

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 in machine learning. He started his career by becoming fluent in Mandarin Chinese and moving to Tibet to do developmental economics fieldwork. He later obtained a graduate degree from Johns Hopkins School of Advanced International Studies.
After switching to the tech industry, Robert's interests shifted to modeling data. He attained his Ph.D. in mathematical statistics from Purdue University, and then he worked as a research engineer in various AI startups. He has published in journals and venues across these spaces, including RECOMB and NeurIPS, on topics including causal inference, probabilistic modeling, sequential decision processes, and dynamic models of complex systems. In addition to startup work, he is a machine learning professor at Northeastern University.


Frequently Asked Questions


When does the course start and finish?
The course opens enrollment periodically. One enrolled, you have access to the instructors four months after enrolling. You will have access to the course materials indefinately.
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.
Who is this course for?
This course is ideal for data scientists and applied machine learning professionals, investors, AI entrepreneurs and AI product managers, and research scientists at any career stage. This course assumes high level familiarity with methods from statistical modeling and machine learning. Logistic regression, deep learning, and topic modeling are methods discussed specifically.
What is the technical background required for this course?
This course is provides a high-level overview. Some references and technical materials are included as supplementary notes for students who want to go deeper.
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 model-based machine learning. 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.