WSN22901 AIOps Lab A

Hiroshima University Syllabus

Japanese

Academic Year 2024Year 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 WSN22901 Subject Classification Specialized Education

Subject Name AIOps演習A（AI系）

Subject Name
（Katakana）エーアイオプスエンシュウエー（エーアイケー）

Subject Name in
English AIOps Lab A

Instructor RAYTCHEV BISSER ROUMENOV

Instructor
(Katakana) ライチェフ　ビセル　ルメノフ

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

Days, Periods, and Classrooms (Int) Inte

Lesson Style Seminar Lesson Style
(More Details) 　

lab based

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 Machine learning, deep learning, Python, PyTorch

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 Learn to implement different machine learning and deep learning models using PyTorch.

Class Schedule lesson1 Basic functionality of PyTorch
lesson2 Basics of machine learning with PyTorch
lesson3 Image recognition with CNNs 1
lesson4 Image recognition with CNNs 2
lesson5 Sequential data analysis with RNNs 1
lesson6 Sequential data analysis with RNNs 2
lesson7 Implementation of computer vision models 1
lesson8 Implementation of computer vision models 2

Project assignments and test

Text/Reference
Books,etc. Jupyter notebooks

PC or AV used in
Class,etc. 　

(More Details)

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review Review basics of Python and especially Numpy (a certain maturity in using Python/Numpy is expected).

Requirements This is a required subject for the AIOps engineer training program. Students who do not take this course cannot take the Internship in AIOps engineer training program.

Grading Method Evaluation will be based on the assignment projects and final test.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other Will be held from 10 September to 13 September.

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	2024Year	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	WSN22901	Subject Classification	Specialized Education
Subject Name	AIOps演習A（AI系）
Subject Name （Katakana）	エーアイオプスエンシュウエー（エーアイケー）
Subject Name in English	AIOps Lab A
Instructor	RAYTCHEV BISSER ROUMENOV
Instructor (Katakana)	ライチェフ　ビセル　ルメノフ
Campus	Higashi-Hiroshima	Semester/Term	1st-Year, First Semester, Intensive
Days, Periods, and Classrooms	(Int) Inte
Lesson Style	Seminar	Lesson Style (More Details)
lab based
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	Machine learning, deep learning, Python, PyTorch
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	Learn to implement different machine learning and deep learning models using PyTorch.
Class Schedule	lesson1 Basic functionality of PyTorch lesson2 Basics of machine learning with PyTorch lesson3 Image recognition with CNNs 1 lesson4 Image recognition with CNNs 2 lesson5 Sequential data analysis with RNNs 1 lesson6 Sequential data analysis with RNNs 2 lesson7 Implementation of computer vision models 1 lesson8 Implementation of computer vision models 2 Project assignments and test
Text/Reference Books,etc.	Jupyter notebooks
PC or AV used in Class,etc.
(More Details)
Learning techniques to be incorporated
Suggestions on Preparation and Review	Review basics of Python and especially Numpy (a certain maturity in using Python/Numpy is expected).
Requirements	This is a required subject for the AIOps engineer training program. Students who do not take this course cannot take the Internship in AIOps engineer training program.
Grading Method	Evaluation will be based on the assignment projects and final test.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other	Will be held from 10 September to 13 September.
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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