Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Fall Semester
Year 2026 366056 Artificial Intelligence and Machine Learning 1 3.0 3 ★ YAO LEEHTER GEE
IEECSG
MSAI 0 0 電機所、電資外生所和創新AI合開,EMI

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	366056	Artificial Intelligence and Machine Learning	1	3.0	3	★	YAO LEEHTER	GEE IEECSG MSAI	0	0	電機所、電資外生所和創新AI合開,EMI

Course Plan：

Instructor YAO LEEHTER

Email ltyao@ntut.edu.tw

Last Updated 2026-06-04 17:27:55

Course Objective 1. Introduction 2. Supervised Learning 3. Bayesian Decision Theory 4. Parametric Methods 5. Multivariate Methods 6. Dimensionality Reduction 7. Clustering 8. Decision Trees 9. Linear Discrimination 10. Kernel Machine

Course Schedule 1. Introduction to Artificial Intelligence and Machine Learning (0.5 Wk) 2. Supervised Learning (1.5 Wk) 3. Bayesian Decision Theory (2 Wk) 4. Parametric Methods (1 Wk) 5. Multivariate Methods (2 Wk) 6. Dimensionality Reduction (3 Wk) 7. Clustering (2 Wk) 8. Decision Trees (2 Wk) 9. Linear Discrimination (2 Wk) 10. Kernel Machines (2 Wk)

Evaluation and grading policy 1st Exam: 30% 2nd Exam: 30% 3rd Exam: 35% Participation: 5%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Introduction to Machine Learning, 4th Edition, Ethem Alpaydin, MIT Press, 2020.

The access to curricular consultation Through office hours.

Expanding teaching and resources ● 無 (None)

The course corresponds to the SDGs SDG4：優質教育（Quality Education）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

Does the course incorporate AI ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）

Note
　

Instructor	YAO LEEHTER
Email	ltyao@ntut.edu.tw
Last Updated	2026-06-04 17:27:55
Course Objective	1. Introduction 2. Supervised Learning 3. Bayesian Decision Theory 4. Parametric Methods 5. Multivariate Methods 6. Dimensionality Reduction 7. Clustering 8. Decision Trees 9. Linear Discrimination 10. Kernel Machine
Course Schedule	1. Introduction to Artificial Intelligence and Machine Learning (0.5 Wk) 2. Supervised Learning (1.5 Wk) 3. Bayesian Decision Theory (2 Wk) 4. Parametric Methods (1 Wk) 5. Multivariate Methods (2 Wk) 6. Dimensionality Reduction (3 Wk) 7. Clustering (2 Wk) 8. Decision Trees (2 Wk) 9. Linear Discrimination (2 Wk) 10. Kernel Machines (2 Wk)
Evaluation and grading policy	1st Exam: 30% 2nd Exam: 30% 3rd Exam: 35% Participation: 5%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Introduction to Machine Learning, 4th Edition, Ethem Alpaydin, MIT Press, 2020.
The access to curricular consultation	Through office hours.
Expanding teaching and resources	● 無 (None)
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate AI	● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
Note