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 359766 Machine Learning 1 3.0 3 ★ Chun-Ling Lin ECE
GAT
IEECSG
MSAI
MSIS 35 0 Jointly offered by Electronic Engineering department.

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	359766	Machine Learning	1	3.0	3	★	Chun-Ling Lin	ECE GAT IEECSG MSAI MSIS	35	0	Jointly offered by Electronic Engineering department.

Course Plan：

Instructor Chun-Ling Lin　Office Hours

Email ginnylin@ntut.edu.tw

Last Updated 2025-12-11 09:07:14

Course Objective 本課程旨在讓學生掌握MATLAB的基本操作與機器學習技術，內容涵蓋數據導入與處理、知識發掘、回歸分析、分類演算法、聚類方法及人工神經網路等主題。此外，課程強調模型效能的優化與實務應用，透過結合理論講解與實作練習，幫助學生培養解決實際問題的能力，最終能夠獨立完成機器學習專案。This course is designed to equip students with fundamental MATLAB operations and machine learning techniques. The content includes data importing and processing, knowledge discovery, regression analysis, classification algorithms, clustering methods, and artificial neural networks. Furthermore, the course emphasizes model performance optimization and practical applications. By combining theoretical instruction with hands-on exercises, students will develop the ability to solve real-world problems and eventually complete independent machine learning projects.

Course Schedule Week 1 Introduction to MATLAB Week 2 Getting Started with MATLAB Machine Learning Week 3 Importing and Organizing Data in MATLAB Week 4 From Data to Knowledge Discovery Week 5 Finding Relationships between Variables - Regression Techniques (Part 1) Week 6 Finding Relationships between Variables - Regression Techniques (Part 2) Week 7 Pattern Recognition through Classification Algorithms (Part 1) Week 8 Pattern Recognition through Classification Algorithms (Part 2) Week 9 Midterm Exam Week 10 Identifying Groups of Data Using Clustering Methods (Part 1) Week 11 Identifying Groups of Data Using Clustering Methods (Part 2) Week 12 Simulation of Human Thinking - Artificial Neural Networks (Part 1) Week 13 Simulation of Human Thinking - Artificial Neural Networks (Part 2) Week 14 Improves the Performance of the Machine Learning Model (Part 1) Week 15 Improves the Performance of the Machine Learning Model (Part 2) Week 16 Machine Learning in Practice (Part 1) Week 17 Machine Learning in Practice (Part 2) Week 18 Final Project Presentation

Evaluation and grading policy 40% Attendance rate + Homework 30% Midterm exam 30% Final project (Oral presentation) or Final exam

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
MATLAB Machine Learning Publisher: Packt Publishing Publication Date: 2017-08-28 Pages: 382 Binding: Paperback

The access to curricular consultation Students can visit Complex Birde 312-2 during office hours for face-to-face discussions or send an email to ginnylin@ntut.edu.tw for course-related inquiries.

The course corresponds to the SDGs SDG4：優質教育（Quality Education）

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

Note
　

Instructor	Chun-Ling Lin　Office Hours
Email	ginnylin@ntut.edu.tw
Last Updated	2025-12-11 09:07:14
Course Objective	本課程旨在讓學生掌握MATLAB的基本操作與機器學習技術，內容涵蓋數據導入與處理、知識發掘、回歸分析、分類演算法、聚類方法及人工神經網路等主題。此外，課程強調模型效能的優化與實務應用，透過結合理論講解與實作練習，幫助學生培養解決實際問題的能力，最終能夠獨立完成機器學習專案。This course is designed to equip students with fundamental MATLAB operations and machine learning techniques. The content includes data importing and processing, knowledge discovery, regression analysis, classification algorithms, clustering methods, and artificial neural networks. Furthermore, the course emphasizes model performance optimization and practical applications. By combining theoretical instruction with hands-on exercises, students will develop the ability to solve real-world problems and eventually complete independent machine learning projects.
Course Schedule	Week 1 Introduction to MATLAB Week 2 Getting Started with MATLAB Machine Learning Week 3 Importing and Organizing Data in MATLAB Week 4 From Data to Knowledge Discovery Week 5 Finding Relationships between Variables - Regression Techniques (Part 1) Week 6 Finding Relationships between Variables - Regression Techniques (Part 2) Week 7 Pattern Recognition through Classification Algorithms (Part 1) Week 8 Pattern Recognition through Classification Algorithms (Part 2) Week 9 Midterm Exam Week 10 Identifying Groups of Data Using Clustering Methods (Part 1) Week 11 Identifying Groups of Data Using Clustering Methods (Part 2) Week 12 Simulation of Human Thinking - Artificial Neural Networks (Part 1) Week 13 Simulation of Human Thinking - Artificial Neural Networks (Part 2) Week 14 Improves the Performance of the Machine Learning Model (Part 1) Week 15 Improves the Performance of the Machine Learning Model (Part 2) Week 16 Machine Learning in Practice (Part 1) Week 17 Machine Learning in Practice (Part 2) Week 18 Final Project Presentation
Evaluation and grading policy	40% Attendance rate + Homework 30% Midterm exam 30% Final project (Oral presentation) or Final exam
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes MATLAB Machine Learning Publisher: Packt Publishing Publication Date: 2017-08-28 Pages: 382 Binding: Paperback
The access to curricular consultation	Students can visit Complex Birde 312-2 during office hours for face-to-face discussions or send an email to ginnylin@ntut.edu.tw for course-related inquiries.
The course corresponds to the SDGs	SDG4：優質教育（Quality Education）
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） ● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction）
Note