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

114-2 356887 人工智能機器學習 1 3.0 3 ★ 陳詩雯自動化所
半導體學位學程
機電博所
機電外國學生專班 0 0 自動化所.機電博含外生班和半導體碩班合開

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
114-2	356887	人工智能機器學習	1	3.0	3	★	陳詩雯	自動化所半導體學位學程機電博所機電外國學生專班	0	0	自動化所.機電博含外生班和半導體碩班合開

教學大綱與進度：

教師姓名陳詩雯　教師諮商時間(Office Hours)

Email shihwen@mail.ntut.edu.tw

最後更新時間 2025-12-17 16:54:31

課程大綱 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.

課程進度 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

評量方式與標準 Homework 40% Mid-term project 30% Final Exam 30%

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
1. Hui Jiang, Machine learning Fundamentals A Concise Introduction, Cambridge, 2021

課程諮詢管道 Email: shihwen@mail.ntut.edu.tw
Office Hours:
Thursday 13:30-17:30
Integrate Technology Complex, Room B09-1

課程對應SDGs指標 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）

課程是否導入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）

備註

教師姓名	陳詩雯　教師諮商時間(Office Hours)
Email	shihwen@mail.ntut.edu.tw
最後更新時間	2025-12-17 16:54:31
課程大綱	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.
課程進度	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
評量方式與標準	Homework 40% Mid-term project 30% Final Exam 30%
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 1. Hui Jiang, Machine learning Fundamentals A Concise Introduction, Cambridge, 2021
課程諮詢管道	Email: shihwen@mail.ntut.edu.tw Office Hours: Thursday 13:30-17:30 Integrate Technology Complex, Room B09-1
課程對應SDGs指標	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）
課程是否導入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）
備註