校際選修

115-1 選課時程

進行中

  • 初選第一階段 6/15/2026
  • 初選第二階段 6/22/2026
  • 校際選修 8/24/2026
  • 初選第三階段 8/31/2026
  • 開學後加退選 9/7/2026
  • 逾期加退選 9/21/2026
選課資源

隨機時序決策與分析

Sequential Decision Modeling and Analytics

學期
113-1
學分
3 學分
當期課號
537410
永久課號
MGEM30084
開課單位
工業工程與管理學系
授課教師
田凱文
校區
光復
類別
選修
上課時間表
週五
2
09:00–09:50
隨機時序決策與分析
MB506
3 節連堂
3
10:10–11:00
4
11:10–12:00

* 根據陽明交大上課時間表所列

概述

This course provides an in-depth exploration of sequential decision making and its industrial applications. Students will gain a solid foundation in stochastic modeling, Markov Decision Processes (MDPs), and Reinforcement Learning (RL), with a focus on practical applications in optimization, automation, and decision-making under uncertainty. Hands-on experience through project work and presentations will ensure students can apply these concepts to real-world industrial problems.

先修科目

Basic probability theory, operations research

教學方式

Python-language programming

評分方式

•Homework Assignments: 10% •Midterm Exam: 30% •Project: 30% •Final Exam: 30%

課程大綱
  • Markov Decision Process (MDP)
  • Reinforcement Learning (RL)
  • Preliminary
週次計畫
週次主題
第 1 週Introduction to Sequential Decision Making and Analytics
第 2 週Stochastic Modeling: Basic Probability, Conditional Probabilities
第 3 週Stochastic Modeling: Markov Chain Properties
第 4 週MDP: Overview of MDP
第 5 週MDP: Policy and Value Functions
第 6 週MDP: Bellman Equations
第 7 週MDP: Finite-Horizon and Infinite-Horizon MDPs
第 8 週MDP: Policy and Value Iteration Methods
第 9 週Midterm Exam
第 10 週RL: Introduction to Reinforcement Learning
第 11 週RL: Q-Learning and SARSA
第 12 週RL: Temporal Difference (TD) Learning
第 13 週Multi-Armed Bandits: Basics and Exploration Strategies
第 14 週Multi-Armed Bandits: Advanced Topics and Thompson sampling
第 15 週Final Exam
第 16 週Project Presentation
教科書

• Ross, Sheldon M. (2014) Introduction to probability models. Academic press. (optional) • Puterman, M. L. (2014). Markov decision processes: discrete stochastic dynamic programming. John Wiley & Sons. (optional) • Sutton R. & Barto A. (2020). Reinforcement Learning: An Introduction (2nd Edition). Cambridge: The MIT Press. (free online)

Office Hours
地點
MB512
時間
Mon. 12:00 – 14:00 (or by appointment)
聯絡方式
kaiwen.tien@nycu.edu.tw