情報科学部Faculty of Computer and Information Sciences
OTR100KA-CS-199（その他 / Others 100）プロジェクト（春）Project
黄 潤和Huang RUNHE
授業コードなどClass code etc
| 学部・研究科Faculty/Graduate school | 情報科学部Faculty of Computer and Information Sciences |
| 添付ファイル名Attached documents | |
| 年度Year | 2023 |
| 授業コードClass code | J0609 |
| 旧授業コードPrevious Class code | |
| 旧科目名Previous Class title | |
| 開講時期Term | 春学期授業/Spring |
| 曜日・時限Day/Period | 金3/Fri.3 |
| 科目種別Class Type | |
| キャンパスCampus | 小金井 / Koganei |
| 教室名称Classroom name | 各学部・研究科等の時間割等で確認 |
| 配当年次Grade | 2～3 |
| 単位数Credit(s) | 1 |
| 備考（履修条件等）Notes | |
| 他学部公開科目Open Program | |
| 他学部公開（履修条件等）Open Program (Notes) | |
| グローバル・オープン科目Global Open Program | |
| 成績優秀者の他学部科目履修制度対象Interdepartmental class taking system for Academic Achievers | |
| 成績優秀者の他学部科目履修（履修条件等）Interdepartmental class taking system for Academic Achievers (Notes) | |
| 実務経験のある教員による授業科目Class taught by instructors with practical experience | |
| SDGsCPSDGs CP | |
| アーバンデザインCPUrban Design CP | |
| ダイバーシティCPDiversity CP | |
| 未来教室CPLearning for the Future CP | |
| カーボンニュートラルCPCarbon Neutral CP | |
| 千代田コンソ単位互換提供（他大学向け）Chiyoda Campus Consortium | |
| 選択・必修Optional/Compulsory | |
| カテゴリー（2022年度以降入学者）Category (2022~) | |
| カテゴリー（2021年度以前入学者）Category (~2021) | |
| カテゴリーCategory |
専門教育科目 専門科目 |
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Outline (in English)
This project is to learn how to use Millimeter Wave Radar
sensors and other sensors for obtaining data of human’s vital
signs and human activities. The obtained data is to be further processed and analyzed based on the technologies of learned AI (artificial intelligence), ML (machine learning), and IoT (Internet of things) technologies for human health monitoring and activity recognition.
The goal is to enable each student masters the basic skills of using Millimeter Wave Radar sensors or other sensors for acquiring data and the fundamental skills of analyzing data so as to solve a small simulated world problem in the fields of healthcare or smart services.
This course will give students a small target problem to solve with AI and ML algorithms. Students are given examples to follow and exercises to do in each class. Students will receive the feedbacks including comments, advice, and suggested references based on the evaluation of their exercises in each class.
The evaluation (100 points) is based on the class participation (28 points) and the final term project including the oral presentation (50 points) and the final report (22 points).
授業で使用する言語Default language used in class
日本語・英語併用 / Japanese & English
授業の概要と目的（何を学ぶか）Outline and objectives
人工知能、機械学習、IoT、およびアプリケーション（I）
（特定の問題を解決するための基本的なスキルを学ぶ）
このプロジェクトは、ミリ波レーダーセンサーやその他のセンサーを使用して、人間のバイタル サインと人間の活動のデータを取得する方法を学習することです。人間の健康モニタリングと活動認識のために、学習した人工知能、機械学習、IoTの技術に基づいてデータを処理と解析することです。
到達目標Goal
The goal is to enable each student masters the basic skills of using Millimeter Wave Radar sensors or other sensors for acquiring data and the fundamental skills of analyzing data so as to solve a small simulated world problem in the fields of healthcare or smart services.
この授業を履修することで学部等のディプロマポリシーに示されたどの能力を習得することができるか（該当授業科目と学位授与方針に明示された学習成果との関連）Which item of the diploma policy will be obtained by taking this class?
情報科学部ディプロマポリシーのうち「DP3-1」と「DP4-3」に関連
授業で使用する言語Default language used in class
日本語・英語併用 / Japanese & English
授業の進め方と方法Method(s)（学期の途中で変更になる場合には、別途提示します。 /If the Method(s) is changed, we will announce the details of any changes. ）
The objective of this project is to make students understand the principles and basic techniques of artificial intelligence (AI) and machine learning (ML) algorithms, and Internet of the things (IoT) techniques. This course will give students a small target problem to solve with AI and ML algorithms. Students are given examples to follow and exercises to do in each class. Students will receive the feedbacks including comments, advice, and suggested references based on the evaluation of their exercises in each class.
アクティブラーニング（グループディスカッション、ディベート等）の実施Active learning in class (Group discussion, Debate.etc.)
あり / Yes
フィールドワーク（学外での実習等）の実施Fieldwork in class
なし / No
授業計画Schedule
授業形態/methods of teaching：対面/face to face
※各回の授業形態は予定です。教員の指示に従ってください。
1[対面/face to face]：Introduction of my research interests and projects
Do exercises based on advice and given teaching reference
2[対面/face to face]：Understand the principles of AI, ML, IoT (1)
Do exercises based on advice and given teaching reference
3[対面/face to face]：Understand the techniques of AI, ML, IoT (2)
Do exercises based on advice and given teaching reference
4[対面/face to face]：Understand the techniques of AI, ML, IoT (3)
Do exercises based on advice and given teaching reference
5[対面/face to face]：Practice (including programming) of AI, ML, or IoT skills (1)
Do exercises based on advice and given teaching reference
6[対面/face to face]：Practice (including programming) of AI, ML, or IoT skills (2)
Do exercises based on advice and given teaching reference
7[対面/face to face]：Practice (including programming) of AI, ML, or IoT skills (3)
Do exercises based on advice and given teaching reference
8[対面/face to face]：Practice (including programming) of AI, ML, or IoT skills (4)
Do exercises based on advice and given teaching reference
9[対面/face to face]：Practice (including programming) of AI, ML, or IoT skills (5)
Do exercises based on advice and given teaching reference
10[対面/face to face]：Practice (including programming) of AI, ML, or IoT skills (6)
Do exercises based on advice and given teaching reference
11[対面/face to face]：Selecting a problem to solve with learned skills
Deciding a term-project
12[対面/face to face]：Term project (1)
Progress on the term project
13[対面/face to face]：Term project (2)
Progress on the term project
14[対面/face to face]：Term project's final representation
Make oral presentation
授業時間外の学習（準備学習・復習・宿題等）Work to be done outside of class (preparation, etc.)
Your required study time is at least one hour for each class meeting.
本授業の毎週の準備・復習時間は、合計１時間以上を標準とします。
テキスト（教科書）Textbooks
Project materials are provided and uploaded to the Hoppii teaching support system.
参考書References
Self-searching for the related references from the Internet based on the project materials provided.
成績評価の方法と基準Grading criteria
The evaluation (100 points) is based on the class participation (28 points) and the final term project including the oral presentation (50 points) and the final report (22 points).
学生の意見等からの気づきChanges following student comments
It is difficult to run the project for 1st, 2nd,and 3rd grades of students together, we have put our efforts on assigning slightly different levels of project tasks to different grades of students.
その他の重要事項Others
If you are interested in knowing what research we are doing. Please do not hesitate to contact with me. You can always reach me by email: rhuang@hosei.ac.jp or by visiting my office: w4022.