Applied Bayesian Machine Learning CS7290 51742
The shortest path to building software for Bayesian reasoning and probabilistic machine learning.
This is a practical Bayesian modeling and probabilistic machine learning workshop designed to familiarize you with a proven model-building workflow. We created this workshop in collaboration with consulting clients who lead data science organizations. We sought to solve two key problems:
- How can we turn our experts' domain knowledge into computable artifacts that we own?
- How can we go beyond simple importing of libraries to building models and model artifacts that grow in value over time?
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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.