1.010 Probability and Causal Inference
Introduces probability and causal inference with an emphasis on understanding, quantifying, and modeling uncertainty and cause-effect relationships in an engineering context. Topics in the first half include events and their probability, the total probability and Bayes' theorems, discrete and continuous random variables and vectors, and conditional analysis. Topics in the second half include covariance, correlation, regression analysis, causality analysis, structural causal models, interventions, and hypothesis testing. Concepts illustrated through data and applications.
This class has 18.02 as a prerequisite.
1.010 will be offered this semester (Fall 2019). It is instructed by S. Saavedra.
Lecture occurs 9:00 AM to 10:30 AM on Tuesdays and Thursdays in 1-242.
This class counts for a total of 12 credits.
You can find more information on MIT OpenCourseWare at the Uncertainty in Engineering site.
© Copyright 2015 Yasyf Mohamedali