Description: A hands-on introduction to probability, statistical inference, regression, Markov chains, queuing theory. Use of an interactive statistical graphics environment such as R.
Course Learning Outcomes: See course objectives.
Program Learning Outcomes
Prerequisites: 241 and 311, or consent.
Textbook(s): An Introduction to Stochastic Modeling (3rd edition). Howard M. Taylor and Samuel Karlin. Academic Press. isbn 0-12-684887-4.
Schedule: Week 1: Introduction and Math refresher Week 2-3: Probability theory Week 4-5: Basic statistics Week 6-7: Bayesian reasoning Week 8-10: Linear regression, regularization, ridge regression, LASSO Week 11: Beyond linear regression models Week 12-14: Advanced statistics Week 15: Final class project