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

113-2 345608 機器學習 1 3.0 3 ★ 林君玲電子所
創新AI學位學程 27 0 電子所和AI班合開

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
113-2	345608	機器學習	1	3.0	3	★	林君玲	電子所創新AI學位學程	27	0	電子所和AI班合開

教學大綱與進度：

教師姓名林君玲　教師諮商時間(Office Hours)

Email ginnylin@ntut.edu.tw

最後更新時間 2024-12-16 09:18:41

課程大綱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.

課程進度 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

評量方式與標準 40% Attendance rate + Homework 30% Midterm exam 30% Final project (Oral presentation) or Final exam

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：是
MATLAB Machine Learning Publisher: Packt Publishing Publication Date: 2017-08-28 Pages: 382 Binding: Paperback

課程諮詢管道 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.

延伸教學與資源

課程對應SDGs指標 SDG4：優質教育（Quality Education）

課程是否導入AI

備註

教師姓名	林君玲　教師諮商時間(Office Hours)
Email	ginnylin@ntut.edu.tw
最後更新時間	2024-12-16 09:18:41
課程大綱	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.
課程進度	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
評量方式與標準	40% Attendance rate + Homework 30% Midterm exam 30% Final project (Oral presentation) or Final exam
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：是 MATLAB Machine Learning Publisher: Packt Publishing Publication Date: 2017-08-28 Pages: 382 Binding: Paperback
課程諮詢管道	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.
延伸教學與資源
課程對應SDGs指標	SDG4：優質教育（Quality Education）
課程是否導入AI
備註