Refactored: Evolve Beyond a Glorified CurveFitter (Special Enrollment)
Mental models for refactoring the machine learning part of your brain
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From zero to modeling hero...
These days, a machine learning workflow typically looks like this:
For many, the craft of data science and machine learning boils to importing from a machine learning bag of tricks,
and tweaking nuances of this training workflow. With enough practice, a practitioner gets paid handsomely to repeatedly build, debug, and rebuild this curvefitting workflow across various problems.
But as the market for glorified curvefitters saturates, how will you and your team add value?
After completing this workshop, practitioners will have had a complete refactoring of their mental frameworks for modeling.
Specifically, they will have
 Seen how to iteratively build a generative machine learning model from scratch using probabilistic programming.
 Used deep learning tools without buying into deep learning hype.
 Gone beyond accuracy and connect model performance directly to a business's key performance indicators.
 The ability to talk about the inductive bias of a model using language from the domain they are modeling.
 Applied mental models from Bayesian decision theory, information theory, Shannon's communication theory, and dynamic systems to model building.
Course Curriculum

StartOvercoming Cognitive Bias with Bespoke Models (7:41)

StartGenerative Probability Models as (Falsifiable) Explanations of Data (6:30)

StartMyths, Models, Programs, Simulations (8:19)

StartBuilding Myths as Causal Bayesian Networks (5:31)

StartProbabilistic Programming and Deep Probabilistic Programming (24:59)
Your Instructor
Robert is currently a research scientist at Microsoft Research and faculty at the Northeastern University department of computer science. He has worked in industry as a research engineer and data scientist building production quality systems for Bayesian decisionmaking under uncertainty.
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.
Robert attained his Ph. D. in mathematical statistics from Purdue University. He has published in top tier journals and venues on topics including causal inference, probabilistic modeling, sequential decision processes, and dynamic models of complex systems.
Frequently Asked Questions
The ML community is in a mental rut. It's time for you to optout.
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Those who complete this course break the mold of engineer/researcher/data scientist. They walk away with unconventional causalitybased mental models for tackling practical problems in practice and research.