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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 curve-fitting workflow across various problems.
But as the market for glorified curve-fitters 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.
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)
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
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.