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?
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 decision-making 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.
This course is closed for enrollment.
StartPrimer on Probability
StartProbability and Computation
StartAssessment: Computational Probability and Monte Carlo
StartData, populations, statistics, and models
StartCase Study: Revisiting Bayes via Training Machine Learning Algorithms
StartCase Study: Revisiting Bayes via Machine Learning In Production
StartFood for thought: Probabilistic Reasoning Systems
StartFood for Thought: Models as Symbolic Explanation Generators for Data