IEOR Courses Acceptable for the Applied Mathematics Major

Department of Mathematics
University of California, Berkeley
Berkeley, California 94720-3840
Tel: (510) 642-0665

July 18, 2005

To: Edward Chang

Dear Mr. Chang:

The Department of Mathematics allows courses from other departments to count towards the Major in Applied Mathematics, provided they form a coherent cluster and have substantial mathematical content. The three courses IEOR 231, 262A and 263A fulfill these criteria and would have been accepted as electives for the Applied Math Major. Course descriptions are below.


Ole Hald
Acting Chair of Mathematics

IEOR 231 Forecasting Models and Time Series Analysis (4 semester credits) Two 1 1/2-hour lectures and one 2-hour laboratory per week. Prerequisites: 263A; Statistics 135. Forecasting Models and Time Series Analysis of discrete time series. The course includes a review of Minimum Mean Squared Error Predictors, Linear Predictors, and the use of Regression Models. Identification and estimation of parameters in autoregressive and moving average processes; linear stationary and non-stationary models; Kalman filters, Bayes estimation and Bayesian forecasting techniques. Updating algorithms for on-line adaptive forecasting.

IEOR 262A Mathematical Programming I (4 semester credits) Three hours of lecture and one hour discussion per week. Prerequisites: Mathematics 111. Basic graduate course in linear programming and introduction to network flows and non-linear programming. Formulation and model building. The simplex method and its variants. Duality theory. Sensitivity analysis, parametric programming, convergence (theoretical and practical). Special structures such as upper bounds and decomposition.

IEOR 263A Applied Stochastic Processes I (4 semester credits) Three hours of lecture and one hour of discussion per week. Prerequisites: Statistics 134A or Statistics 200A. Conditional Expectation. Poisson and renewal processes. Renewal reward processes with application to inventory, congestion, and replacement models. Discrete and continuous time Markov chains; with applications to various stochastic systems – such as exponential queueing systems, inventory models and reliability systems.