國立中興大學教學大綱
課程名稱 (中) 人工智慧應用實務(1304)
(Eng.) Practical Applications of Artificial Intelligence
開課單位 智慧創意學程
課程類別 必修 學分 3 授課教師 李宏中
選課單位 智慧創意學程 / 學士班 授課使用語言 英文 英文/EMI Y 開課學期 1132
課程簡述 The basic concepts of Deep learning algorithms would be introduced. Also, the students would understand how to implement those methods to solve the problems occurred in our daily life.

Self-directed Learning I: Meta Learning (https://reurl.cc/D4eWj5)
Self-directed Learning II: Federated Learning (https://reurl.cc/eLnjyW)
先修課程名稱
課程含自主學習 Y
課程與核心能力關聯配比(%) 課程目標之教學方法與評量方法
課程目標 核心能力 配比(%) 教學方法 評量方法
This course intends to introduce the concepts of Deep learning (DL) and its implementations. Also, each student should learn how to code and apply the algorithms so as to analyze the real-world problems.
專題探討/製作
習作
討論
講授
出席狀況
口頭報告
作業
授課內容(單元名稱與內容、習作/每週授課、考試進度-共18週)
週次 授課內容
第1週 Introduction to Deep Learning (DL)
第2週 Introduction to Deep Learning Type - Regression
第3週 Introduction to Deep Learning Type - Classification
第4週 Implementation – Part I (Regression) & (Classification)
第5週 Introduction to Ensemble Learning
第6週 Implementation – Part I (Ensemble Learning)
第7週 Implementation – Part II (Regression) & (Classification)
第8週 Midterm-Project Presentation
第9週 Implementation – Part II (Ensemble Learning)
第10週 Introduction to Data Visualization
第11週 Implementation – Data Visualization
第12週 Introduction to Semi-supervised Learning
第13週 Implementation – Semi-supervised Learning
第14週 Introduction to Transfer Learning
第15週 Implementation – Transfer Learning
第16週 Final-Project Presentation
第17週 Self-directed Learning I - Participate in relevant lectures, workshops, or seminars using materials designated by the instructors.
第18週 Self-directed Learning II - Participate in relevant lectures, workshops, or seminars using materials designated by the instructors.
學習評量方式
Midterm-Project Presentation (30%), Final-Project Presentation (30%), Participation/Assignment (20%), In Class Presentation (20%)
教科書&參考書目(書名、作者、書局、代理商、說明)
Main Textbook: Handout
● Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2023). Dive into deep learning. Cambridge University Press.
● Gulli, A., & Kapoor, A. (2017). TensorFlow 1. x Deep Learning Cookbook: Over 90 unique recipes to solve artificial-intelligence driven problems with Python. Packt Publishing Ltd.
課程教材(教師個人網址請列在本校內之網址)
The course materials would be found at ilearn.
課程輔導時間
Tuesday 10:00-11:00 am or please contact the teacher to arrange a meeting time.
聯合國全球永續發展目標(連結網址)
04.教育品質提供體驗課程:N
請尊重智慧財產權及性別平等意識,不得非法影印他人著作。
更新日期 西元年/月/日:無 列印日期 西元年/月/日:2025 / 3 / 14
MyTB教科書訂購平台:http://www.mytb.com.tw/