Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2026 356279 Information Retrieval and AI Applications 1 3.0 3 ★ CHI NAI-WEN GCDPE 9 0 土木208

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	356279	Information Retrieval and AI Applications	1	3.0	3	★	CHI NAI-WEN	GCDPE	9	0	土木208

Course Plan：

Instructor CHI NAI-WEN　Office Hours

Email nwchi@ntut.edu.tw

Last Updated 2025-12-20 15:43:33

Course Objective 資訊檢索（Information Retrieval）是搜尋引擎背後的技術基礎，以給定的查詢詞彙找出帶有相關資訊的文件，能夠處理與比較未結構化或半結構化的文字資料。在人工智慧與機器學習技術飛越成長的當代，自然語言處理（Natural Language Processing）及文本探勘（Text Mining）輔助人們在散亂的文本當中發掘有意義的資訊。傳統的資訊檢索理論在文本探勘的任務當中則扮演了重要的輔助角色，其賦與將文件以向量表達的基礎，亦即使諸多人工智慧應用能夠順利的與文本探勘任務接軌。本課程將教導修課同學資訊檢索、自然語言處理與資料探勘的理論基礎，以及各種新穎的人工智慧與機器學習應用如何取代傳統方法。並且訓練修課同學使用現成的程式語言套件來分析自己手邊的文本資料。 Information Retrieval (IR) is the process that retrieves the information which is relevant to a specific information need from a data collection. Search engine systems provides a famous example of IR applications. In recent years, with the growth of Artificial Intelligence, Machine Learning, and Natural Language Processing (NLP), many of the AI applications can help discover useful information in unstructured or semi-structured text collections, such a process is called Text Mining. The traditional IR technique also play an important role to support the AI applications. It can vectorize the texts and make it possible to compare the similarities between different pieces of text. This course aims to train the enrolled students to learn the basic concept of IR, NLP, and AI applications which are relevant to Text Mining tasks. In addition, this course also trains the enrolled students to use existing tools and software packages which can support the AI applications.

Course Schedule 1 課程介紹 2-3 資訊檢索的理論基礎 4-6 類神經網路的基礎 7-8 捲積神經網路與YOLO 9 期中提案報告 10-11 循環神經網路：RNN, LSTM與GRU 12-13 seq2seq問題與Encoder-Decoder架構 14-15 Attention：注意力機制與Transformer 16-17 生成式AI與多模態AI 18 期末報告

Evaluation and grading policy 課堂表現(35%)、作業(35%)、期末報告（30%）

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
C. D. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval, Cambridge University Press, 2008. Christopher D. Manning and Hinrich Schütze, Foundations of Statistical Natural Language Processing, The MIT Press, 1999.

The access to curricular consultation nwchi@ntut.edu.tw

Expanding teaching and resources

The course corresponds to the SDGs 無（None）

Does the course incorporate AI ● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process）
● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction）
● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）
● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）

Note
　

Instructor	CHI NAI-WEN　Office Hours
Email	nwchi@ntut.edu.tw
Last Updated	2025-12-20 15:43:33
Course Objective	資訊檢索（Information Retrieval）是搜尋引擎背後的技術基礎，以給定的查詢詞彙找出帶有相關資訊的文件，能夠處理與比較未結構化或半結構化的文字資料。在人工智慧與機器學習技術飛越成長的當代，自然語言處理（Natural Language Processing）及文本探勘（Text Mining）輔助人們在散亂的文本當中發掘有意義的資訊。傳統的資訊檢索理論在文本探勘的任務當中則扮演了重要的輔助角色，其賦與將文件以向量表達的基礎，亦即使諸多人工智慧應用能夠順利的與文本探勘任務接軌。本課程將教導修課同學資訊檢索、自然語言處理與資料探勘的理論基礎，以及各種新穎的人工智慧與機器學習應用如何取代傳統方法。並且訓練修課同學使用現成的程式語言套件來分析自己手邊的文本資料。 Information Retrieval (IR) is the process that retrieves the information which is relevant to a specific information need from a data collection. Search engine systems provides a famous example of IR applications. In recent years, with the growth of Artificial Intelligence, Machine Learning, and Natural Language Processing (NLP), many of the AI applications can help discover useful information in unstructured or semi-structured text collections, such a process is called Text Mining. The traditional IR technique also play an important role to support the AI applications. It can vectorize the texts and make it possible to compare the similarities between different pieces of text. This course aims to train the enrolled students to learn the basic concept of IR, NLP, and AI applications which are relevant to Text Mining tasks. In addition, this course also trains the enrolled students to use existing tools and software packages which can support the AI applications.
Course Schedule	1 課程介紹 2-3 資訊檢索的理論基礎 4-6 類神經網路的基礎 7-8 捲積神經網路與YOLO 9 期中提案報告 10-11 循環神經網路：RNN, LSTM與GRU 12-13 seq2seq問題與Encoder-Decoder架構 14-15 Attention：注意力機制與Transformer 16-17 生成式AI與多模態AI 18 期末報告
Evaluation and grading policy	課堂表現(35%)、作業(35%)、期末報告（30%）
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes C. D. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval, Cambridge University Press, 2008. Christopher D. Manning and Hinrich Schütze, Foundations of Statistical Natural Language Processing, The MIT Press, 1999.
The access to curricular consultation	nwchi@ntut.edu.tw
Expanding teaching and resources
The course corresponds to the SDGs	無（None）
Does the course incorporate AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction） ● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
Note