In this course, you will learn:
- To construct systematic frameworks for decision-making in a dynamic situation, fundamental principles from probability, statistics, stochastic modelling, and optimization are used.
- How to learn the underlying model and pattern using historical data.
- In business applications, optimization methods and software are used to address decision issues under uncertainty.
- Introduction to Probability: Random variables; Normal, Binomial, Exponential distributions; applications
- Estimation: sampling; confidence intervals; hypothesis testing
- Regression: linear regression; dummy variables; applications
- Linear Optimization; Non-linear optimization; Discrete Optimization; applications
- Dynamic Optimization; decision trees