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 programming language course so I will not teach you Python from scratch. Instead, sample codes for lecture problems will be clearly 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 get your hands dirty and write the program.
Finally, I highly encourage students to ask me questions in- and off-class whenever you don’t understand my lectures. I urge students NOT to ask for solutions to homework problems. Be open-minded to LISTEN to each other, be proactive to share, and think out-of-the-box.
| 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 1 (Sep 11) |
Discrete distributions |
|
Class 2 (Sep 18) |
Continuous distributions |
|
Class 3 (Sep 25) |
Stochastic dependencies |
|
Class 4 (Oct 02) |
Optimal decisions |
|
Class 5 (Oct 16) |
Dynamic models |
|
Class 6 (Oct 30) |
Guest speaker 1910-2000 |
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!
Lecture notes will be provided.
https://wm5.nccu.edu.tw/mooc/index.php