OM 420/620 Predictive Business Analytics, Undergraduate/MBA
Application of predictive statistical models in various areas. Students will learn how to extract data from relational databases, prepare the data for analysis, and build basic predictive models using data mining software. Emphasizes the practical use of analytical tools to improve decisions rather than algorithm details.
OM 502 Operations Management, MBA
This course focuses on (1) the competitive advantage that a business unit can derive from innovative and efficient production and delivery of its goods and services and on (2) analytical approaches that are useful in understanding and improving an organization's operations. Specific modules include process diagramming and analysis; measuring and managing flow times; inventory control and optimization; supply chain coordination and operations strategy. Cases will be used to illustrate operational efficiency and its significance to the profitability of a firm.
IE 801 Markov Decision Processes and Principles of Reinforcement Learning, Graduate, Fall 2024 while visiting KAIST
By the end of the course, you will have a solid grasp of the main ideas in sequential decision-making and reinforcement learning. Any student who understands the material in this course will understand the mathematical formulation of sequential decision-making as a Markov decision process and dynamic programming as a solution method. Based on these, the students will understand the foundations of many modern reinforcement learning algorithms. That person will be able to apply these tools and ideas in novel situations, e.g., to determine whether the methods apply to a specific situation and, if so, how to apply them.Â
IOE 202 Operations Modeling, Undergraduate, Fall 2013 during my PhD program
Topics: Probability, Deterministic and stochastic inventory control, Demand forecasting, Linear programming, Integer programming, Simulation, Queuing theory, Decision tree