Handle complex decisions with ease and confidence using powerful mathematical concept in this course taught by an award-winning mathematician.
Mathematical Decision Making: Predictive Models and Optimization
Overview
Syllabus
- By This Professor
- 01: The Operations Research Superhighway
- 02: Forecasting with Simple Linear Regression
- 03: Nonlinear Trends and Multiple Regression
- 04: Time Series Forecasting
- 05: Data Mining-Exploration and Prediction
- 06: Data Mining for Affinity and Clustering
- 07: Optimization-Goals, Decisions, and Constraints
- 08: Linear Programming and Optimal Network Flow
- 09: Scheduling and Multiperiod Planning
- 10: Visualizing Solutions to Linear Programs
- 11: Solving Linear Programs in a Spreadsheet
- 12: Sensitivity Analysis-Trust the Answer?
- 13: Integer Programming-All or Nothing
- 14: Where Is the Efficiency Frontier?
- 15: Programs with Multiple Goals
- 16: Optimization in a Nonlinear Landscape
- 17: Nonlinear Models-Best Location, Best Pricing
- 18: Randomness, Probability, and Expectation
- 19: Decision Trees-Which Scenario Is Best?
- 20: Bayesian Analysis of New Information
- 21: Markov Models-How a Random Walk Evolves
- 22: Queuing-Why Waiting Lines Work or Fail
- 23: Monte Carlo Simulation for a Better Job Bid
- 24: Stochastic Optimization and Risk
Taught by
Scott P. Stevens
