|Advanced Statistics for AI||Hours: 3 0 3|
Review of probability and distributions, tests of hypothesis, types of errors, create and interpret data visualizations using the Python programming language and associated packages & libraries, apply statistical modeling techniques to data (i.e., linear and logistic regression, linear models, multilevel models, Bayesian inference techniques), apply and interpret inferential procedures when analyzing real data, understand the importance of connecting research questions to data analysis methods.
|Pre-requisites: ES202||Co-requisites: AI|
Hours: XYZ where X = Lecture, Y = Lab, Z = Credit
All hours are per week.
3 Lab hours constitute 1 credit hour
1 credit hour implies 1 lecture of 50mins per academic week. 16 weeks in total.
Pre-Requisite courses are courses required to be completed before this course may be taken
Co-Requisite courses are courses required to be taken along with this course