Covers random variables, sample spaces, probability distributions, functions, expected value, variance, and estimations.
Covers the concept of mean-square-error inference and optimal estimators for inference problems using different design criteria.
Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.
Explores continuous culture using a chemostat, covering design, operation, advantages, productivity, applications, and comparisons with batch cultures.
