| 课程号 |
00333145 |
学分 |
3 |
| 英文名称 |
Simulation Methods for Optimization and Learning |
| 先修课程 |
无 |
| 中文简介 |
本课程讲解了使用计算机模拟进行动态随机模型分析与优化的要点。课程的重点放在利用离散事件动态系统来模拟随机系统，以及通过离散事件仿真分析和改进其模拟的表现。课程内容的应用广泛分布于各种领域，从社交网络到计算机网络，从金融工程到商业过程。本课程将通过基于模拟仿真的优化和学习方法来为学生介绍计算机模拟在动态随机模型分析中的应用。课程要回答的首要问题是如何使用模拟仿真来为现实生活中的问题做出更优、更负责任的决策。此外，课程还将回顾针对上述问题我们所见证的技术与数学理论上的进步。在积极设计模拟仿真课题的同时，课程还将会花时间回顾相应的数学与技术模型。亦即，在学习实用技术的同时，课程鼓励学生去思考与总结相关的科学与技术发展成果。 |
| 英文简介 |
This course gives a broad treatment of the important aspects of the use of computer simulation for the analysis and optimization of dynamic stochastic models. The emphasis is on modeling the stochastic system as a discrete event dynamic system, and analyzing and improving its performance by means of discrete event simulation. Applications will stem from a wide range of domains: from Social Networks to Computer Networks, and Financial Engineering to Business Processes. The course will introduce students to the use of computer simulation in analyzing dynamic stochastic models through simulation-based methods for optimization and learning. The leading question of the course is how to use simulation to make better and more responsible decisions for real-life problems. The course will also reflect on the technological and mathematical developments we witness in our societies. While actively working on simulation projects, the course will provide space for reflecting on the mathematical/technological paradigm. That is, next to learning the actual techniques, students will be stimulated to reflect on the history of science and the technological developments around them. |
| 开课院系 |
工学院 |
| 通选课领域 |
|
| 是否属于艺术与美育 |
否 |
| 平台课性质 |
|
| 平台课类型 |
|
| 授课语言 |
英文 |
| 教材 |
无；
Simulation Modeling and Analysis,C. Cassandras and S. Lafortune, Springer,Handbook of Monte Carlo Methods,D. Kroese, T. Taimre, Z. Botev, Wiley,2011,Introduction to Discrete Event Systems,Mc Graw Hill, |
| 参考书 |
4th or 5th；
；
2nd；
|
| 教学大纲 |
Students learn how to model and analyze real-life problems by Monte Carlo simulation. After successful completion of this course, students will be able to conduct a Monte Carlo simulation based analysis of a problem, provide an output analysis, and place their research into the broader historical and societal context.
1. Programming language is Python (basic programs will be provided). Other programming languages, such as Matlab, are also fine but are not supported.
2. Basics of Monte Carlo Simulation: random number generation, discrete event simulation, output analysis
3. Standard simulation models: queuing systems, social networks, financial products, inventory systems, news vendor problem
4. Data and simulation: combining simulation with available historical data
5. Estimation of gradients via simulation and their application in learning and optimization: stochastic gradient method, stochastic approximation, supervised learning, non-supervised learning
6. Reflection on the technological developments: history of mathematics, philosophical, ethical and sociological aspects of the mathematical/technological paradigm
课堂授课
学生上课需自备笔记本电脑
Presentation and written report 30%
Simulation project written report 30%
Final exam 30%
Attendance and discussion 10%
Total 100%
|
| 教学评估 |
Bernd HEIDERGOTT：
学年度学期：18-19-3，课程班：优化和学习的模拟方法1，课程推荐得分：null，教师推荐得分：null，课程得分分数段：80及以下；
学年度学期：20-21-3，课程班：优化和学习的模拟方法1，课程推荐得分：null，教师推荐得分：null，课程得分分数段：null；
学年度学期：22-23-3，课程班：优化和学习的模拟方法1，课程推荐得分：null，教师推荐得分：null，课程得分分数段：80及以下；
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