ECE 3700 Probability and Inference 3.0 Credits
An introduction to probabilistic reasoning and statistical inference with applications in data analysis and machine learning. Topics include probability models, conditional probability and independence, discrete and continuous random variables (univariate and multivariate), expectation, and probabilistic modeling. Fundamental limit theorems, including the laws of large numbers and the central limit theorem, are emphasized. The course also covers statistical inference techniques such as point estimation, hypothesis testing, maximum likelihood estimation, and Bayesian inference.
Repeat Status: Not repeatable for credit
Prerequisites: MATH 2901 [Min Grade: D] or ECE 232 [Min Grade: D] or CAEE 232 [Min Grade: D] or MATH 210 [Min Grade: D] or MATH 262 [Min Grade: D] or ENGR 232 [Min Grade: D]