How to Evolve Beyond Glorified Curve-Fitter
Mental models for refactoring the machine learning part of your brain
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.
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.