查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

115-1 366414 智慧醫療大數據實作與分析 1 3.0 3 ★ 李建樂創新AI學位學程 3 0 計中電腦教室

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
115-1	366414	智慧醫療大數據實作與分析	1	3.0	3	★	李建樂	創新AI學位學程	3	0	計中電腦教室

教學大綱與進度：

教師姓名李建樂　教師諮商時間(Office Hours)

Email leecy@ntut.edu.tw

最後更新時間 2026-06-03 23:19:02

課程大綱本課程旨在介紹智慧醫療領域中常見之生醫大數據分析技術與應用，培養學生運用資訊科技解決醫療與生命科學問題之能力。課程內容涵蓋基礎生物學概念、生物資訊的經典演算法、基因體學與轉錄體學資料分析流程，以及微陣列（Microarray）、次世代定序（Next-Generation Sequencing, NGS）等高通量生物技術。學生將學習DNA定序分析、RNA定序分析及總體基因體（Metagenomics）分析方法，並透過實作熟悉生醫大數據分析流程。此外，課程邀請生醫領域專家進行專題演講，介紹電子病歷（Electronic Health Records, EHR）、基因體學（Genomics）、代謝體學（Metabolomics）等智慧醫療大數據之實務應用與發展趨勢。透過案例分析與閱讀期刊討論，培養學生整合生醫資料與人工智慧技術之能力。 This course provides a comprehensive introduction to biomedical big data analytics and its applications in smart healthcare. Topics include fundamental biological concepts, classical bioinformatics algorithms, genomic and transcriptomic data analysis, and high-throughput technologies such as Microarray and Next-Generation Sequencing (NGS). Students will learn the principles and practical workflows of DNA-seq, RNA-seq, and metagenomic analyses through hands-on exercises and real-world datasets. The course also features invited lectures from biomedical experts covering emerging applications of healthcare big data, including Electronic Health Records (EHRs), Genomics, Metabolomics, and other multi-omics data. Through case studies, journal paper discussions, and practical projects, students will gain experience in integrating biomedical data analytics with artificial intelligence techniques to address challenges in healthcare, precision medicine, and biomedical research.

課程進度 Week 1: Introduction Week 2-3: Concept of biology Week 4: NO CLASS (Teachers' Day) Week 5: Concept of biology Week 6: Special lecture Week 7: Sequencing alignment & Microarray technologies Week 8: NO CLASS (Taiwan Retrocession Day) Week 9: Introduction of sequencing technologies Week 10-11: DNA-seq analysis Week 12-13: RNA-seq analysis Week 14-15: Metagenomic analysis Week 16-17: Special lectures Week 18: Final report

評量方式與標準 Practical group exercise Group journal presentation Reflection report Class attendance & involvement

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：否
1. Campbell Biology 12/e, Lisa U. et al., Pearson. 2. Concepts of Biology, Samantha Fowler et al., OpenStax. 3. Biology (2/e), Mary A. Clark et al., OpenStax. 4. Bioinformatics: A Practical Guide to Next Generation Sequencing Data Analysis, Hamid D. Ismail, CRC Press. 5. Bioinformatics: A Practical Guide to NCBI Databases and Sequence Alignments, Hamid D. Ismail, CRC Press. 6. 生物人的電腦教室,李鎮宇 (Eric Lee), 碁峰資訊 7. Developing Bioinformatics Computer Skills, Per Jambeck & Cynthia Gibas, O'Reilly.

課程諮詢管道 You can consult about the course in the Online Discussion of iSchool+. If you prefer to discuss privately, it's recommended to use email or arrange an in-person meeting during the teacher's consultation hours.

延伸教學與資源 ● 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.
● 數位教材資源應用 (Application of Digital Resources)
採用影音多媒體或具開放授權之資源（如OCW、開放教科書）作為主要或輔助教材。
Adopts multimedia or open-licensed resources (e.g., OCW, Open Textbooks) as primary or supplementary teaching materials.

課程對應SDGs指標 SDG3：良好健康和福祉（Good Health and Well-being）
SDG4：優質教育（Quality Education）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

課程是否導入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）
● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction）

備註 EMI Course --- 彈性學習一、類別學生分組報告遠距教學二、內容 (1)第 17 週：邀請專家學者進行遠距教學，講授與智慧醫療相關臨床應用，每位學生於課後提出一份心得報告。 (2)第 18 週：各組選定一篇與課程相關之近期研究期刊進行報告分享，全班參與 Q&A 提問討論。三、時數總共 6 小時。四、學習成果第 17 週上課後每位學生需繳交心得報告一份、第 18 週上課前需繳交PPT簡報檔。五、評量比例 (1)心得報告：10% (2)口頭報告與PPT簡報：30%

教師姓名	李建樂　教師諮商時間(Office Hours)
Email	leecy@ntut.edu.tw
最後更新時間	2026-06-03 23:19:02
課程大綱	本課程旨在介紹智慧醫療領域中常見之生醫大數據分析技術與應用，培養學生運用資訊科技解決醫療與生命科學問題之能力。課程內容涵蓋基礎生物學概念、生物資訊的經典演算法、基因體學與轉錄體學資料分析流程，以及微陣列（Microarray）、次世代定序（Next-Generation Sequencing, NGS）等高通量生物技術。學生將學習DNA定序分析、RNA定序分析及總體基因體（Metagenomics）分析方法，並透過實作熟悉生醫大數據分析流程。此外，課程邀請生醫領域專家進行專題演講，介紹電子病歷（Electronic Health Records, EHR）、基因體學（Genomics）、代謝體學（Metabolomics）等智慧醫療大數據之實務應用與發展趨勢。透過案例分析與閱讀期刊討論，培養學生整合生醫資料與人工智慧技術之能力。 This course provides a comprehensive introduction to biomedical big data analytics and its applications in smart healthcare. Topics include fundamental biological concepts, classical bioinformatics algorithms, genomic and transcriptomic data analysis, and high-throughput technologies such as Microarray and Next-Generation Sequencing (NGS). Students will learn the principles and practical workflows of DNA-seq, RNA-seq, and metagenomic analyses through hands-on exercises and real-world datasets. The course also features invited lectures from biomedical experts covering emerging applications of healthcare big data, including Electronic Health Records (EHRs), Genomics, Metabolomics, and other multi-omics data. Through case studies, journal paper discussions, and practical projects, students will gain experience in integrating biomedical data analytics with artificial intelligence techniques to address challenges in healthcare, precision medicine, and biomedical research.
課程進度	Week 1: Introduction Week 2-3: Concept of biology Week 4: NO CLASS (Teachers' Day) Week 5: Concept of biology Week 6: Special lecture Week 7: Sequencing alignment & Microarray technologies Week 8: NO CLASS (Taiwan Retrocession Day) Week 9: Introduction of sequencing technologies Week 10-11: DNA-seq analysis Week 12-13: RNA-seq analysis Week 14-15: Metagenomic analysis Week 16-17: Special lectures Week 18: Final report
評量方式與標準	Practical group exercise Group journal presentation Reflection report Class attendance & involvement
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：否 1. Campbell Biology 12/e, Lisa U. et al., Pearson. 2. Concepts of Biology, Samantha Fowler et al., OpenStax. 3. Biology (2/e), Mary A. Clark et al., OpenStax. 4. Bioinformatics: A Practical Guide to Next Generation Sequencing Data Analysis, Hamid D. Ismail, CRC Press. 5. Bioinformatics: A Practical Guide to NCBI Databases and Sequence Alignments, Hamid D. Ismail, CRC Press. 6. 生物人的電腦教室,李鎮宇 (Eric Lee), 碁峰資訊 7. Developing Bioinformatics Computer Skills, Per Jambeck & Cynthia Gibas, O'Reilly.
課程諮詢管道	You can consult about the course in the Online Discussion of iSchool+. If you prefer to discuss privately, it's recommended to use email or arrange an in-person meeting during the teacher's consultation hours.
延伸教學與資源	● 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. ● 數位教材資源應用 (Application of Digital Resources) 採用影音多媒體或具開放授權之資源（如OCW、開放教科書）作為主要或輔助教材。 Adopts multimedia or open-licensed resources (e.g., OCW, Open Textbooks) as primary or supplementary teaching materials.
課程對應SDGs指標	SDG3：良好健康和福祉（Good Health and Well-being） SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
課程是否導入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） ● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction）
備註	EMI Course --- 彈性學習一、類別學生分組報告遠距教學二、內容 (1)第 17 週：邀請專家學者進行遠距教學，講授與智慧醫療相關臨床應用，每位學生於課後提出一份心得報告。 (2)第 18 週：各組選定一篇與課程相關之近期研究期刊進行報告分享，全班參與 Q&A 提問討論。三、時數總共 6 小時。四、學習成果第 17 週上課後每位學生需繳交心得報告一份、第 18 週上課前需繳交PPT簡報檔。五、評量比例 (1)心得報告：10% (2)口頭報告與PPT簡報：30%