Bayesian Reasoning in Data Science (PhD Course)#

Prof. C. Fanelli - DATA 644 8 (last update: May 8/2024)


Course Syllabus

Synopsis: No data scientist can work without a solid grasp of conditional probability and Bayesian reasoning. Bayes’ theorem allows to update our beliefs based on the occurrence of new events, steering the inference towards the truth and assessing uncertainty in predictions. This course provides an introduction to Bayesian Reasoning in Data Science (BRDS) and will let you appreciate the basic building blocks of this approach through real-world examples across different areas. During the course you will learn concrete computational implementations, that will help students connect what they have read and heard with what they can program, reinforcing the material.

This course is based on the following references [D'Agostini, 2003, Davidson-Pilon, 2015, Downey, 2021, MacKay et al., 2003, Martin, 2018]

Additional resources