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 356887 Artificial intelligent and machine learning 1 3.0 3 ★ Shih-Wen Chen GAT
半導體學位學程
DME
機電外國學生專班 21 0 自動化所.機電博含外生班和半導體碩班合開

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	356887	Artificial intelligent and machine learning	1	3.0	3	★	Shih-Wen Chen	GAT 半導體學位學程 DME 機電外國學生專班	21	0	自動化所.機電博含外生班和半導體碩班合開

Course Plan：

Instructor Shih-Wen Chen　Office Hours

Email shihwen@mail.ntut.edu.tw

Last Updated 2025-12-17 16:54:31

Course Objective Introduction to machine learning by Python programming. Regression analysis, dimension reduction, feature extraction, classification methods, neural networks, and LLM. Python examples to improve students' knowledge of machine learning.

Course Schedule week1: Introduction to machine learning week2: Probability and optimization week3: Feature Extraction week4: Bayesian decision theory week5: Regression week6: Neural Networks week7: CNNs week8: Hidden Markov Models week9: Mid-term presentation week10: LSTM and RNN week11: Attentions week12: Generative Models week13: Transformers week14: Autoencoders week15: Graphical NN week16: DDPM week17: Case Study week18: Final exam

Evaluation and grading policy Homework 40% Mid-term project 30% Final Exam 30%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
1. Hui Jiang, Machine learning Fundamentals A Concise Introduction, Cambridge, 2021

The access to curricular consultation Email: shihwen@mail.ntut.edu.tw
Office Hours:
Thursday 13:30-17:30
Integrate Technology Complex, Room B09-1

The course corresponds to the SDGs SDG3：良好健康和福祉（Good Health and Well-being）
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	Shih-Wen Chen　Office Hours
Email	shihwen@mail.ntut.edu.tw
Last Updated	2025-12-17 16:54:31
Course Objective	Introduction to machine learning by Python programming. Regression analysis, dimension reduction, feature extraction, classification methods, neural networks, and LLM. Python examples to improve students' knowledge of machine learning.
Course Schedule	week1: Introduction to machine learning week2: Probability and optimization week3: Feature Extraction week4: Bayesian decision theory week5: Regression week6: Neural Networks week7: CNNs week8: Hidden Markov Models week9: Mid-term presentation week10: LSTM and RNN week11: Attentions week12: Generative Models week13: Transformers week14: Autoencoders week15: Graphical NN week16: DDPM week17: Case Study week18: Final exam
Evaluation and grading policy	Homework 40% Mid-term project 30% Final Exam 30%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes 1. Hui Jiang, Machine learning Fundamentals A Concise Introduction, Cambridge, 2021
The access to curricular consultation	Email: shihwen@mail.ntut.edu.tw Office Hours: Thursday 13:30-17:30 Integrate Technology Complex, Room B09-1
The course corresponds to the SDGs	SDG3：良好健康和福祉（Good Health and Well-being） 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