Refactored: A Primer on Inductive Biases

A concise primer on "invariances", "priors" and other "inductive biases" in machine learning and deep neural net architectures.

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Your Instructor


Robert Osazuwa Ness
Robert Osazuwa Ness

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.


Course Curriculum


  Primer on Inductive Biases in Machine Learning
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