GISDepartment of Global and Interdisciplinary Studies
MAN300ZA（経営学 / Management 300）Impact of Artificial IntelligenceImpact of Artificial Intelligence
MAYMAY HOMaymay HO
授業コードなどClass code etc
| 学部・研究科Faculty/Graduate school | GISDepartment of Global and Interdisciplinary Studies |
| 添付ファイル名Attached documents | |
| 年度Year | 2022 |
| 授業コードClass code | A6329 |
| 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 | 水3/Wed.3 |
| 科目種別Class Type | |
| キャンパスCampus | 市ヶ谷 / Ichigaya |
| 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 | 3～4 |
| 単位数Credit(s) | 2 |
| 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) | 制度ウェブサイトの3．科目別の注意事項 (1) GIS主催科目の履修上の注意を参照すること。 |
| 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 | |
| 旧科目との重複履修Duplicate Subjects Taken Under Previous Class Title | |
| カテゴリー（2024年度以降入学者）Category (commenced 2024 onwards) | |
| カテゴリー（2020～2023年度入学者）Category (commenced 2020-2023) | |
| カテゴリー（2016～2019年度入学者）Category (commenced 2016-2019) |
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授業の概要と目的（何を学ぶか）Outline and objectives
Artificial Intelligence (AI) has a profound impact on the business world in many ways, changing the way cities are run, the way we live and socialise through to the way we do business. This course focuses on how businesses use AI to make their businesses more profitable and customer experience better. In case-studies we will cover during this course we will analyse the impact and thereby also understanding businesses better. We will also observe that businesses employ data scientists to analyse data. These scientists use machine learning as part of their implementation of AI. So in the later part of the course we will delve deeper into Machine Learning so that we can better understand what data scientists do. Hence we are able to understand the “mechanics” of AI.
到達目標Goal
Using the critical thinking exercises and class discussions, students will be able to apply their knowledge to case-studies and group work. The skills they acquire through this course should prepare them to understand key technical terms and give a better understanding of the world.
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?
Will be able to gain “DP 1”, “DP 2”, “DP 3”, and “DP 4”.
授業で使用する言語Default language used in class
英語 / 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. ）
At the beginning of class, feedback for the previous class is given using some comments from submitted reaction papers. Method of instruction will be a mixture of lecture, group presentation and discussions.
Submission of assignments and feedback will be via the Learning Management System.
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[オンライン/online]：Introduction
Introduction to Artificial Intelligence.
2[対面/face to face]：Robotics in Business
Introduction to Robotics in Business.
3[対面/face to face]：AI to Improve Customer Experience
Discuss on how AI improves customer experience.
4[対面/face to face]：AI to Allow Entrepreneurship
Discuss on how AI encourages entrepreneurship.
5[対面/face to face]：Review of Class Materials
Review of class materials.
6[対面/face to face]：AI to Drive Business Performance
Discuss how AI drives business performance.
7[対面/face to face]：AI in Healthcare
Discuss how AI drives in healthcare industry.
8[対面/face to face]：Hacking, Fraud and Cybercrime
Discuss the impact on hacking, fraud and cybercrime.
9[対面/face to face]：Machine Learning In Business and Regression Revisited
Revise the regression. Discuss machine learning in business.
10[対面/face to face]：Hands on Demonstration of R Language
Perform demonstration of R language.
11[対面/face to face]：Hands on Demonstration on Microsoft Machine Learning
Perform demonstration on microsoft machine learning.
12[対面/face to face]：AI and Current Affairs
Discuss AI and current affairs.
13[対面/face to face]：Discussion and Review
Discussion and review.
14[対面/face to face]：Wrap-up & Review of Class Materials.
Review of Class Materials.
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.)
Students are expected to read the assigned readings and slides of the next class before each class. Also, in addition to the preparation for the final presentation, there will be homework during the course. Preparatory study and review time for this class are 2 hours each. Additional reading on the daily news and related research articles are highly recommended.
テキスト（教科書）Textbooks
Electronic slides will be provided.
参考書References
References will be provided in class slides.
成績評価の方法と基準Grading criteria
15% Quizzes
15% Projects / homework
35% Midterm exam
35% Final examination
学生の意見等からの気づきChanges following student comments
None.
学生が準備すべき機器他Equipment student needs to prepare
None.
その他の重要事項Others
None.
Prerequisite
None.