Course Syllabus

Course Information：

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	364153	Introduction to Information Retrieval	1	3.0	3	★	Jenq-Haur Wang	GCSIE IEECSG	0	0	EMI,資工四、資工所和電資外生所合開

Course Plan：

Instructor	Jenq-Haur Wang　Office Hours
Email	jhwang@ntut.edu.tw
Last Updated	2026-06-03 13:04:52
Course Objective	Web search engine has become one of the necessary tools in modern lives. This course aims at investigating how to index large amount of text documents and search what users need in an effective way. We will introduce the basic concepts of information retrieval, characteristics of documents and languages, various retrieval models, evaluation of retrieval results, indexing and searching, text classification, text clustering, and Web search. In addition to texts, we will also introduce some applications of multimedia retrieval.
Course Schedule	1. Indexing (3 wks) 2. Basic text modeling and evaluation (2-3 wks) 3. Query processing and advanced models (2-3 wks) 4. Machine learning in IR (2 wks) 5. Web search (2-3 wks) 6. Advanced topics: CLIR, IE, social Web, ... (1 wk) 7. Term project presentation (2-3 wks) Flexible Learning: (2 wks) 1. Group-based program implementation and discussions: for make-up homeworks, and extra bonus
Evaluation and grading policy	Homework assignments and programming exercises: ~40% Mid-term exam: ~25-30% Term project and presentation: ~30-35% Extra bonus: <=10% (for makeup homeworks in flexible learning)
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes - Textbook: . Christopher D. Manning, Prabhakar Raghavan and Hinrich Schuetze, Introduction to Information Retrieval, Cambridge University Press. 2008. (available online at: https://www.informationretrieval.org/) - References: . Ricardo Baeza-Yates and Berthier Ribeiro-Neto, Modern Information Retrieval: the concepts and technology behind search, 2nd ed. Addison-Wesley, 2011. . Bruce Croft, Donald Metzler, and Trevor Strohman, Search Engines: Information Retrieval in Practice, Addison-Wesley, 2010. . Stefan Buettcher, Charles L.A. Clarke, and Gordon V. Cormack, Information Retrieval: Implementing and Evaluating Search Engines, MIT Press, 2010. Prerequisite: . Basic knowledge of data structures and algorithms . Programming experience is necessary for homeworks & projects - The latest information will be available soon at: https://myweb.ntut.edu.tw/~jhwang/IR/
The access to curricular consultation	The teacher can be reached via e-mail or messages in Microsoft Teams. The TA will help you join the course group in Facebook where you can also ask questions about the homeworks and exams.
Expanding teaching and resources	● 混成學習 (Blended Learning) 課程結合實體授課與線上學習（同步視訊／非同步活動）。 Combines face-to-face instruction with online learning (including synchronous video sessions or asynchronous activities). ● 數位科技融入 (Digital Technology Integration) 運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。 Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment.
The course corresponds to the SDGs	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
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） ● 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	All course sessions will be recorded online and put on iSchool+ afterwards, along with all course materials, homework announcements and submissions. The following channels will be set up: 1. Microsoft Teams: you will be automatically added to the team for your enrolled courses in Teams (<StudentID>@cc.ntut.edu.tw). 2. Facebook: the TA can set up a group for the course in Facebook where you can also ask questions about the homeworks and exams. Flexible Learning: (Wk #17-#18) Group-based program implementation and discussions: for make-up homeworks, and extra bonus (<=10%)