How to Evolve Beyond Glorified Curve-Fitter

Mental models for refactoring the machine learning part of your brain

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From zero to causal modeling hero...

...by way of mental models for machine learning that distinguish you from the pack.

The cultural norm in the machine learning community is to focus on hacks and tricks to improve predictive accuracy. As an increasing number of engineers are indoctrinated into this mindset, the market is becoming saturated with curve-fitters.

Those who cultivate alternative mental frameworks for modeling can evolve beyond curve-fitting, commanding a premium in the market for engineers, data scientists, and researchers.

After this course, you will have become an engineer/manager/data scientist who has high-level mental models for machine learning that will

  • distinguish you from the pack
  • avoid pitfalls in thinking
  • increase ROI on your career, research, skill-upleveling.

Enrollment is currently closed

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?
Course enrollment opens periodically. You have access to the instructor for four months after the course opens. You will have indefinite access to the course materials thereafter.
How long do I have access to the course materials?
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 is a high-level course. Anyone who is comfortable thinking quantitatively about data is suited for this course. The "Do Causality like a Bayesian" section will unavoidably introduce some mathematical notation. For those seeking just a high level understanding of the material, they can safely skip over the notation and focus on the common English explanations.
I am a manager/investor after a high-level overview that can inform stategic decisions. Is this for me?
Yes. In contrast with other causal modeling courses at this school, this one stays at a high level. This course provides mental models for thinking about causality in the context of machine learning. For strategic decision-makers, the models will contextualize causal reasoning within an organization's machine learning objectives.
I already know all about Bayes and generative machine learning. Will this course benefit me?
Very probably yes. The course goes beyond low-level Bayesian math to high-level mental frameworks for modeling. If you have some exposure to the math, you will still make connections that people don't typically 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.

The ML community is in a mental rut. It's time for you to opt-out.

Break free from trend-following with AltDeep's radical approach to machine learning.

Those who complete this course break the mold of engineer/researcher/data scientist. They walk away with unconventional causality-based mental models for tackling practical problems in practice and research.

This course is closed for enrollment.