WSN23301 AIOps Lab D

Hiroshima University Syllabus

Japanese

Academic Year 2025Year School/Graduate School Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Informatics and Data Science Program

Lecture Code WSN23301 Subject Classification Specialized Education

Subject Name AIOps演習D（制御系）

Subject Name
（Katakana）エーアイオプスエンシュウディー（セイギョケイ）

Subject Name in
English AIOps Lab D

Instructor LI MENGMOU

Instructor
(Katakana) リ　メンモ

Campus Higashi-Hiroshima Semester/Term 1st-Year, First Semester, Intensive

Days, Periods, and Classrooms (Int) Inte

Lesson Style Seminar Lesson Style
(More Details) Face-to-face

Exercise

Credits 1.0 Class Hours/Week 　 Language of Instruction B : Japanese／English

Course Level 7 : Graduate Special Studies

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 02 : Information Science

Eligible Students Graduate students who have registered for the AIOps engineer training program

Keywords Control, Optimization, Algorithms

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students)

Class Objectives
/Class Outline The course aims to teach you how to apply control and optimization methods to solve real-world problems.

Class Schedule lesson1 Introduction to Control and Optimization
lesson2 Control simulation exercises using Python
lesson3 Optimization and basic algorithms
lesson4 Linear Optimal Control (LQR) and Implementation
lesson5 Model Predictive Control (MPC) and Implementation
lesson6 Reinforcement Learning (RL) and Implementation
lesson7 Exercise 1
lesson8 Exercise 2 & Final presentation
lesson9
lesson10
lesson11
lesson12
lesson13
lesson14
lesson15

Text/Reference
Books,etc. Boyd, S. P., & Vandenberghe, L. (2004). Convex optimization. Cambridge university press.
Bertsekas, D. (2012). Dynamic programming and optimal control: Volume I (Vol. 4). Athena scientific.

PC or AV used in
Class,etc.

(More Details)

Learning techniques to be incorporated

Suggestions on
Preparation and
Review Basic knowledge of linear algebra, calculus and Python is required.

Requirements This lecture is required by AIOps engineer training program. The students who do not take this lecture may not take "Internship" in AIOps engineer training program.

The lecture will be conducted during four days. The date on lecture will be announced through Momiji.

Practice in teams of 2 to 3 people throughout the lecture.

Grading Method Project assignments and test

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2025Year	School/Graduate School	Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Informatics and Data Science Program
Lecture Code	WSN23301	Subject Classification	Specialized Education
Subject Name	AIOps演習D（制御系）
Subject Name （Katakana）	エーアイオプスエンシュウディー（セイギョケイ）
Subject Name in English	AIOps Lab D
Instructor	LI MENGMOU
Instructor (Katakana)	リ　メンモ
Campus	Higashi-Hiroshima	Semester/Term	1st-Year, First Semester, Intensive
Days, Periods, and Classrooms	(Int) Inte
Lesson Style	Seminar	Lesson Style (More Details)	Face-to-face
Exercise
Credits	1.0	Class Hours/Week		Language of Instruction	B : Japanese／English
Course Level	7 : Graduate Special Studies
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	02 : Information Science
Eligible Students	Graduate students who have registered for the AIOps engineer training program
Keywords	Control, Optimization, Algorithms
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)
Class Objectives /Class Outline	The course aims to teach you how to apply control and optimization methods to solve real-world problems.
Class Schedule	lesson1 Introduction to Control and Optimization lesson2 Control simulation exercises using Python lesson3 Optimization and basic algorithms lesson4 Linear Optimal Control (LQR) and Implementation lesson5 Model Predictive Control (MPC) and Implementation lesson6 Reinforcement Learning (RL) and Implementation lesson7 Exercise 1 lesson8 Exercise 2 & Final presentation lesson9 lesson10 lesson11 lesson12 lesson13 lesson14 lesson15
Text/Reference Books,etc.	Boyd, S. P., & Vandenberghe, L. (2004). Convex optimization. Cambridge university press. Bertsekas, D. (2012). Dynamic programming and optimal control: Volume I (Vol. 4). Athena scientific.
PC or AV used in Class,etc.
(More Details)
Learning techniques to be incorporated
Suggestions on Preparation and Review	Basic knowledge of linear algebra, calculus and Python is required.
Requirements	This lecture is required by AIOps engineer training program. The students who do not take this lecture may not take "Internship" in AIOps engineer training program. The lecture will be conducted during four days. The date on lecture will be announced through Momiji. Practice in teams of 2 to 3 people throughout the lecture.
Grading Method	Project assignments and test
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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