市ヶ谷リベラルアーツセンター（ILAC）ILAC Course
PRI100LA（情報学基礎 / Principles of informatics 100）Elementary Information TechnologyElementary Information Technology
松田 裕幸Yukou MATSUDA
授業コードなどClass code etc
| 学部・研究科Faculty/Graduate school | 市ヶ谷リベラルアーツセンター（ILAC）ILAC Course |
| 添付ファイル名Attached documents | |
| 年度Year | 2022 |
| 授業コードClass code | P0162 |
| 旧授業コードPrevious Class code | |
| 旧科目名Previous Class title | |
| 開講時期Term | 秋学期授業/Fall |
| 曜日・時限Day/Period | 木3/Thu.3 |
| 科目種別Class Type | |
| キャンパスCampus | 市ヶ谷 |
| 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 | GBP/SCOPE 1～4年 |
| 単位数Credit(s) | 2 |
| 備考（履修条件等）Notes | 選基・情／※定員制 |
| 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（2017年度以降）Category (2018~) |
2017年度以降入学者 ILAC科目 100番台 選択基盤科目 0群（自校教育、基礎ゼミ、情報、キャリア教育関連科目等） |
| カテゴリー（2016年度以前）Category (2017) |
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Outline (in English)
You will understand the essence of the current popular IT topics including the Internet, computer system, data science and the artificial intelligence.
授業で使用する言語Default language used in class
英語 / English
授業の概要と目的（何を学ぶか）Outline and objectives
You will learn the information and communication technology with Python programming.
到達目標Goal
You will understand the essence of the current popular IT topics including the Internet, computer system, data science and the artificial intelligence.
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?
各学部のディプロマ・ポリシーのうち、以下に関連している。法学部・法律学科：DP3・DP4、法学部・政治学科：DP1、法学部・国際政治学科：DP1、文学部：DP1、経営学部：DP3、国際文化学部：DP4、人間環境学部：DP2、キャリアデザイン学部：DP1
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. ）
The class materials will be available in the online notebooks. Teaching style is basically face-to-face, however simply applicable to the online style due to the online notebooks. Each class is taught in the text mixed with programs and assign a short homework.
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]：Python Programming
You will learn to be able to read Python code.
2[対面/face to face]：Computer System and the Internet
You will understand how computer works and the Internet works.
3[対面/face to face]：Data Encoding
Computed data including characters should be converted to machine readable code.
4[対面/face to face]：Data Science [1]
You will learn DataFrame. Python based data handling tool DataFrame performs Excel like actions and more.
5[対面/face to face]：Data Science [2]
You understand the trend with data viewing.
6[対面/face to face]：Data Science [3]
You will learn how to draw map.
7[対面/face to face]：Data Science [4]
You will learn how to plot data on the map.
8[対面/face to face]：Machine Learning [1]
You will learn how to predict with linear regression.
9[対面/face to face]：Machine Learning [2]
You will learn how to predict with k-nearest neighborhood.
10[対面/face to face]：Machine Learning [3]
Big example: MNIST. You will learn how to classify hand written digits.
11[対面/face to face]：Machine Learning [4]
You will learn how to classify hand written digits with Perceptron.
12[対面/face to face]：Information Encoding [1]
Encryption.
13[対面/face to face]：Information Encoding [2]
Credit Card/Bar Code/QR code
14[対面/face to face]：Summary
You are given the review of the whole classes.
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.)
No special work will be assigned to you. However you need to finish all the homework assigned in the class. Preparatory study and review time for this class are 2 hours each.
テキスト（教科書）Textbooks
None.
参考書References
All texts are uploaded in HOPPII.
成績評価の方法と基準Grading criteria
To pass the study quality and to get the grade, you need attend the whole classes and submit all the homeworks. The quality of the last homework will dominate 80% of the score and the 20% of the score depends on homeworks issued on every classes. You need get more than 60 points for the total 100 points to pass this class.
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
My career introduction. I have been designing, implementing automatic programming and teaching human knowledge into computer, especially in natural language.