Type of Credit: Elective
Credit(s)
Number of Students
In many business problem situations, managers collect data to shape their beliefs about uncertain parameters and outcomes. Simulation is perhaps the most important modeling technique being used to facilitate decision-making under uncertainty. With that premise, this course exposes students to discrete and continuous probability distributions and simulation that are crucial for evaluating decisions in a stochastic environment. We will analyze numerous decision problems that can be solved by simulation analysis.
能力項目說明
The primary goal of this course is to sharpen students’ quantitative modeling capabilities to give an edge to your career. After taking this course, students are expected to have a good understanding of Monte-Carlo simulation methods grounded on subjective and objective data. Lecture notes and assigned readings will be in https://wm5.nccu.edu.tw/mooc/index.php
Python programming will be part of this learning process. Note that this is NOT a Python language course so I will not teach you Python from scratch. Instead, sample codes for lecture problems will be explained and provided. To make our life easier, we will use Colaboratory developed by Google (https://colab.research.google.com/). The only way to maximize learning efficacy is to generate the program. Remember, now we have a good friend called ChatGPT. Coding is less crucial as defining problems, objectives, variables, and parameters.
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
Class Schedule
|
Class 1 (Sep 09) |
Discrete distributions |
|
Class 2 (Sep 16) |
Continuous distributions |
|
Class 3 (Sep 23) |
Stochastic dependencies |
|
Class 4 (Sep 30) |
Optimal decisions |
|
Class 5 (Oct 07) |
Dynamic models |
Grading: I reserve the right to adjust score allocation rules.
Homework: 40% I expect to distribute 2 assignments (20% each).
Final Project: 40% Don’t be a free rider. Form your team wisely.
Upload your code & report onto WM5. Please keep your report succinct, clear, & logical
Participation: 20%
I am open to questions, ideas, and thoughts. Just speak out!
I will distribute my own notes.