Courses

 [Operations Research I (IMEN 260)] 

Objective: This course introduces the concept of operations research and applications.  It mainly covers a variety of topics in the linear programming; formulation, solution method, theoretical analysis and applications

Course outline:

1. Overview of Operations Research
2. Introduction to LP
3. Solving LP problems : Simplex Method
4. Theory of Simplex Method
5. Duality Theory
6. Transportation and Assignment Problems
7. Network Optimization Problems
8. Dynamic Programming
9. Integer Programming

 [Simulation (IMEN 481)] 

Objective: To provide basic concepts of system modeling and simulation, characteristics of the discrete systems, simulation techniques, statistical methods to analyze the results, and application to real-life problems.

Course outline:

1. Fundamental simulation concepts
2. Simulation language
3. Verification & validation
4. Data collection and selecting input probability distribution
5. Random number generation
6. Output data analysis
7. Variance reduction techniques

 [Dynamic Programming  and Reinforcement Learning Applications (IMEN 764)] 

Objective: To provide the student with the art of formulating recursive equations and the method that can solve many of optimization problems involving sequential decision making.

Course outline:

1. Introduction (Sequential Decision Process, Principle of Optimality)
2. Deterministic DP : DP Algorithm
3. Deterministic DP : Shortest Path Problem and Other Applications
4. Deterministic DP : DP and Hamilton-Jacobi-Bellman (HJB) Eq.
5. Stochastic DP : Introduction to Markov Decision Process (MDP)
6. Stochastic DP : Short Review on Discrete Time Markov Chain (DTMC)
7. Stochastic DP : MDP Applications
8. Stochastic DP : Finite-Horizon MDP
9. Stochastic DP : Infinite-Horizon MDP
10. Stochastic DP : Discounted MDP and Policy/Value Iterations
11. Approximate Dynamic Programming
12. Reinforcement Learning : Temporal Difference Learning, On-Policy/Off-Policy Learning
13. Reinforcement Learning : Value Function Approximation, Basics of Deep Q Learning

 [Game Theory and Business Applications (IMEN 582)] 

Objective: To provide the student with knowledge on the interactions among stakeholders (decision makers, agents) in several real-world business operation situations, and the resulting dynamics in a market environment. In particular, there is a focus on the following issues:

– Understanding game theory models and related concepts

– Analyze game theory models: Computing equilibria

– Understanding the classifications of interactions among stakeholders

– Using above models and concepts into some applications in various disciplines

Course outline:

1. Introduction of Game Theory
2. Static game of complete information : Cournot competition, Betrand competition, etc.
3. Dynamic game of complete information : Stakelberg game, Repeated game, Imperfect information, etc.
4. Static game of incomplete information : Bayesian game, Asymmetric information game, Auction, etc.
5. Dynamic game of incomplete information : Signaling game, Principal-Agent problem etc.
6. Business Applications : Supply chain coordination, Auction Theory, Principal-Agent Problem, Mechanism Design etc.

 [Probability & Statistics for Engineers (IMEN 272)] 

Objective: This course introduces students to standard methods of describing and analyzing data, probability theory, statistical inference, and ordinary least squares. The understanding of fundamental concepts and rules of probability and statistics is applied to engineering problems

Course outline:

1. Chapter 1: Introduction
2. Chapter 2: Probability
3. Chapter 3: Random Variables and Probability Distributions
4. Chapter 4: Mathematical Expectation
5. Chapter 5: Some Discrete Probability Distributions
6. Chapter 6: Some Continuous Probability Distributions
7. Chapter 7: Functions of Random Variables
8. Chapter 8: Fundamental Sampling Distributions and Data Descriptions
9. Chapter 9: One- and Two-Sample Estimation Problems
10. Chapter 10: One- and Two-Sample Tests of Hypotheses
11. Chapter 11: Simple Linear Regression and Correlation