NCHU Course Outline

Course Name	(中) 數據分析與機器學習應用(3319)
	(Eng.) Data Analysis and Applied Machine Learning
Offering Dept	Department of Applied Mathematics (Data Science and Computing Program)
Course Type	Elective	Credits	3	Teacher	CHANG-YUN LIN
Department	Department of Applied Mathematics (Data Science and Computing Program)/Undergraduate	Language	English	Semester	2026-FALL
Course Description	學習如何使用機器學習進行資料分析 Learning how to use machine learning for data analysis
Prerequisites				self-directed learning in the course	Y

Relevance of Course Objectives and Core Learning Outcomes(%)			Teaching and Assessment Methods for Course Objectives
Course Objectives	Competency Indicators	Ratio(%)	Teaching Methods	Assessment Methods
學習如何使用機器學習進行資料分析 Learning how to use machine learning for data analysis			Lecturing topic Discussion/Production Discussion	Assignment Quiz Written Presentation Attendance Oral Presentation Study Outcome Internship
Course Content and Homework/Schedule/Tests Schedule
Week Course Content Week 1 Course Introduction, Environment Setup, and Introduction to Generative AI-Assisted Learning 課程簡介、環境建置與生成式 AI 輔助學習導論 Week 2 Python Programming Fundamentals and Data Structures Python 程式設計基礎與資料結構 Week 3 Data Preprocessing and Applications of Exploratory Data Analysis (EDA) Packages 資料前處理與探索性資料分析 (EDA) 套件應用 Week 4 Hands-on Data Visualization and Chart Interpretation 資料視覺化實作與圖表解析 Week 5 Supervised Machine Learning Models (I): Regression Analysis and Classification 監督式機器學習模型（一）：迴歸分析與分類問題 Week 6 Supervised Machine Learning Models (II): Decision Trees and Ensemble Learning 監督式機器學習模型（二）：決策樹與整合學習 Week 7 Unsupervised Machine Learning Models: Clustering and Dimensionality Reduction Algorithms 非監督式機器學習模型：分群與降維演算法 Week 8 Model Evaluation, Cross-Validation, and Midterm Practical Assessment 模型評估、交叉驗證與期中綜合實作檢核 Week 9 Problem Definition and Requirements Analysis 問題定義與需求分析 Week 10 Literature Review and Data Cleaning 文獻蒐集與資料清理 Week 11 Machine Learning Model Building and Model Evaluation 建立機器學習模型與模型評估 Week 12 Exploratory Data Analysis and Feature Engineering 探索性資料分析與特徵工程 Week 13 Model Comparison and Optimization 模型比較與最佳化 Week 14 Visualization of Results 成果視覺化 Week 15 Project Integration and Presentation Preparation 專案統整與簡報製作 Week 16 Final Project Presentation 專題成果展示 self-directed learning    03.Preparing presentations or reports related to industry and academia.
Evaluation
Quizzes & Attendance (~50%): Makeup exam score × 60% Learning Notes & Assignments (~10%) Major Exams & Projects (~30%) Participation & Others (~10%) Note: Please check your grades regularly on your own. Grade inquiries will not be accepted after the semester ends. Final grades will be adjusted (Maximum: 96 / No extra credit or remedial options available. 小考/出席率（～50%）：補考成績*60% 學習筆記/作業（～10%） 大考/報告（～30%） 其他（～10%） 平常請自行確認成績/學期結束後不開放查詢 總成績會做調整/最高96分/無補救方式
Textbook & other References
「Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow 2/e」，by Aurélien Géron, 2019
Teaching Aids & Teacher's Website
https://www.youtube.com/channel/UCSivAooQ-OTLATS1dTT3DZw
Office Hours
After class
Sustainable Development Goals, SDGs(Link URL)
include experience courses：N

Please respect the intellectual property rights and use the materials legally.Please respect gender equality.
Update Date, year/month/day：2026/09/10 18:03:36	Printed Date, year/month/day：2026 / 10 / 26
The second-hand book website：http://www.myub.com.tw/