KA237003 Informatics and Data Science Exercise III(Intelligence Science Program)

Hiroshima University Syllabus

Japanese

Academic Year 2024Year School/Graduate School School of Informatics and Data Science

Lecture Code KA237003 Subject Classification Specialized Education

Subject Name 情報科学演習III（知能科学プログラム）

Subject Name
（Katakana）ジョウホウカガクエンシュウ３（チノウカガクプログラム）

Subject Name in
English Informatics and Data Science Exercise III(Intelligence Science Program)

Instructor RAYTCHEV BISSER ROUMENOV,TAKAFUJI DAISUKE

Instructor
(Katakana) ライチェフ　ビセル　ルメノフ,タカフジ　ダイスケ

Campus Higashi-Hiroshima Semester/Term 3rd-Year, Second Semester, 3Term

Days, Periods, and Classrooms (3T) Mon5-7：East Library 3F Seminar Rm D

Lesson Style Seminar Lesson Style
(More Details) 　

Labs, face-to-face

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

Course Level 3 : Undergraduate High-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 02 : Information Science

Eligible Students

Keywords data analysis, machine learning, media content analysis

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) Computer Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.
・D3. Knowledge of hardware and software and programming ability to process data efficiently.

Data Science Program
（Knowledge and Understanding）
・D1. Knowledge and ability to understand the theoretical framework of statistics and data analysis and to analyze qualitative/quantitative information of big data accurately and efficiently.
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.

Intelligence Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.
（Comprehensive Abilities）
・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science.

Class Objectives
/Class Outline In this class, students engage in exercises on specialized and practical content based on the knowledge that has been widely learned in previous lectures of the Faculty of Information Science. Students learn the ability to find solutions for given exercises and problems, to deal with them, and to summarize the results as reports in this class. During weeks 2-4 labs will be conducted on topic 1 “Data analytics”, while during weeks 5-7 labs on topic 2 “Media content analysis” will be conducted. Through these labs the students will be able to obtain knowledge and practical experience necessary for the analysis of both general and various media-related data like images and audio data.

Class Schedule week 1. Guidance (Please gather at East Library Seminar Room D)
week 2. Data analysis with Pandas (Raytchev)
week 3. Visualization and regression (Raytchev)
week 4. Classification and clustering methods (Raytchev)
week 5. Music signal processing (Yu)
week 6. Feature extraction and visualization (Yu)
week 7. Content-based music retrieval (Yu)
week 8. Final exam (Topic 1 and 2)

assignments and final test

Text/Reference
Books,etc. Follow the instructions of the instructor for each topic.

PC or AV used in
Class,etc. 　

(More Details)

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review Relevant information about each topic will be given during the guidance session.

Requirements

Grading Method Evaluation based on a comprehensive assessment of the reports and final exam for each topic.

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	2024Year	School/Graduate School	School of Informatics and Data Science
Lecture Code	KA237003	Subject Classification	Specialized Education
Subject Name	情報科学演習III（知能科学プログラム）
Subject Name （Katakana）	ジョウホウカガクエンシュウ３（チノウカガクプログラム）
Subject Name in English	Informatics and Data Science Exercise III(Intelligence Science Program)
Instructor	RAYTCHEV BISSER ROUMENOV,TAKAFUJI DAISUKE
Instructor (Katakana)	ライチェフ　ビセル　ルメノフ,タカフジ　ダイスケ
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, Second Semester, 3Term
Days, Periods, and Classrooms	(3T) Mon5-7：East Library 3F Seminar Rm D
Lesson Style	Seminar	Lesson Style (More Details)
Labs, face-to-face
Credits	1.0	Class Hours/Week		Language of Instruction	B : Japanese／English
Course Level	3 : Undergraduate High-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	02 : Information Science
Eligible Students
Keywords	data analysis, machine learning, media content analysis
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)	Computer Science Program （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis. ・D3. Knowledge of hardware and software and programming ability to process data efficiently. Data Science Program （Knowledge and Understanding）・D1. Knowledge and ability to understand the theoretical framework of statistics and data analysis and to analyze qualitative/quantitative information of big data accurately and efficiently. （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis. Intelligence Science Program （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis. （Comprehensive Abilities）・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science.
Class Objectives /Class Outline	In this class, students engage in exercises on specialized and practical content based on the knowledge that has been widely learned in previous lectures of the Faculty of Information Science. Students learn the ability to find solutions for given exercises and problems, to deal with them, and to summarize the results as reports in this class. During weeks 2-4 labs will be conducted on topic 1 “Data analytics”, while during weeks 5-7 labs on topic 2 “Media content analysis” will be conducted. Through these labs the students will be able to obtain knowledge and practical experience necessary for the analysis of both general and various media-related data like images and audio data.
Class Schedule	week 1. Guidance (Please gather at East Library Seminar Room D) week 2. Data analysis with Pandas (Raytchev) week 3. Visualization and regression (Raytchev) week 4. Classification and clustering methods (Raytchev) week 5. Music signal processing (Yu) week 6. Feature extraction and visualization (Yu) week 7. Content-based music retrieval (Yu) week 8. Final exam (Topic 1 and 2) assignments and final test
Text/Reference Books,etc.	Follow the instructions of the instructor for each topic.
PC or AV used in Class,etc.
(More Details)
Learning techniques to be incorporated
Suggestions on Preparation and Review	Relevant information about each topic will be given during the guidance session.
Requirements
Grading Method	Evaluation based on a comprehensive assessment of the reports and final exam for each topic.
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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