Website: http://people.cs.vt.edu/~bhuang/courses/causal20/

Important machine learning scientists, such as Turing Awardee Judea Pearl, 
have long argued that much of the progress in machine learning has been limited 
to function approximation. Popular learning algorithms fit models to data, but 
have no inherent understanding of the phenomena that generate the data. Instead, 
these scientists argue, smarter algorithms need to reason about causality: 
which variables cause change in other variables. In this course, we will explore 
together the state of knowledge on causal modeling approaches and identify whether 
there is indeed hope for smarter artificial intelligence through this concept.

Prerequisites: CS 5804 OR CS 5814