教學大綱 Syllabus

科目名稱:人工智慧與數位人文

Course Name: Artificial Intelligence and Digital Humanities

修別:群

Type of Credit: Partially Required

3.0

學分數

Credit(s)

30

預收人數

Number of Students

課程資料Course Details

課程簡介Course Description

* 每堂課皆需自備筆電至課堂上課

(TBA) This course targets on students who have zero background in programming, and aims to provide them with basic skills in processing the data themselves.

The goal is to let students know how to import packages with simple scripts to help further analyze linguistic data. In addition, basic machine learning applications will be introduced as well. Here, in this course, we’ll focus on handling text data with Python, which is one of the prevalent programming languages nowadays.

At the end of this course, students will need to do a final presentation and submit a final project.

核心能力分析圖 Core Competence Analysis Chart

能力項目說明


    課程目標與學習成效Course Objectives & Learning Outcomes

    Students will learn how to write basic Python scripts to help process text data, and analyze linguistic data more efficiently. Basic machine learning applications will be introduced for students to have a general idea of how linguistic knowledge facilitate AI learning.

    每周課程進度與作業要求 Course Schedule & Requirements

    (TBA) 此課堂每週皆為程式實作課程,必須每堂自備筆電至教室上課。

    Week 1 - Course introduction: Why Python?  |  Install Anaconda & Python 3

    Week 2 - Variables and Data Types | interactive mode & IDE

    Week 3 - Basic Data Structure: List, Tuple

    Week 4 - Basic Data Structure: Dataframe

    Week 5 - Regular Expression & Loop

    Week 6 - Loop & Function

    Week 7 - NLTK toolkit & Segmentation

    Week 8 - Day Off (校慶)

    Week 9 - Visualization

    Week 10 - Machine learning: Decision Tree

    Week 11 - Machine learning: SVM

    Week 12 - HTML introduction

    Week 13 - Crawler

    Week 14 - Gradio

    Week 15 - Flask

    Week 16 - Final Project Discussion

    Week 17 - Presentation

    Week 18 - Term Project Due

    授課方式Teaching Approach

    35%

    講述 Lecture

    20%

    討論 Discussion

    20%

    小組活動 Group activity

    20%

    數位學習 E-learning

    5%

    其他: Others:

    評量工具與策略、評分標準成效Evaluation Criteria

    (Tentative)

    課程參與率40%: 包含出席率、課堂討論、小組討論參與率

    課堂練習 30%: 課堂練習完成度

    期末展演 20%: 期末成果口頭報告

    期末報告 10%: 期末成果呈現完成度

     

    *嚴格禁止抄襲

    *無故缺席不得超過三次

    指定/參考書目Textbook & References

    Mark Lutz. (2013). Learning Python. O'Reilly Media, Inc. https://www.oreilly.com/library/view/learning-python-5th/9781449355722/

    Wes McKinney. (2017). Python for Data Analysis. O'Reilly Media, Inc. https://www.oreilly.com/library/view/python-for-data/9781491957653/

    Steven Bird, Ewan Klein, and Edward Loper. (2009). Natural Language Processing with Python. O'Reilly Media, Inc. https://www.nltk.org/book/

    已申請之圖書館指定參考書目 圖書館指定參考書查詢 |相關處理要點

    維護智慧財產權,務必使用正版書籍。 Respect Copyright.

    課程相關連結Course Related Links

    
                

    課程附件Course Attachments

    課程進行中,使用智慧型手機、平板等隨身設備 To Use Smart Devices During the Class

    Yes

    列印