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 359993 Technical English Writing 1 3.0 3 ★ Yu-Hsiu Lin GAT 12 0 上限15人

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	359993	Technical English Writing	1	3.0	3	★	Yu-Hsiu Lin	GAT	12	0	上限15人

Course Plan：

Instructor Yu-Hsiu Lin　Office Hours

Email yhlin@ntut.edu.tw

Last Updated 2025-12-18 17:20:08

Course Objective This semester's Deep Learning (DL) course will focus on Machine Learning (ML), DL, and Generative AI in data science analytics. We will look at supervised, unsupervised and structured learning methods, which will include classic Multi-Layer Perceptron (MLP) as a shallow or Deep Neural Network (DNN), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), k-Means clustering, and Generative Adversarial Networks (GANs). This course is designed for graduate students who are interested in AI and its applications in data science analytics. Students who take this course should have mathematical maturity of undergraduate curricula in science and engineering, including Calculus, Discrete Mathematics, Linear Algebra and programming language such as C/C++/MATLAB/Python. Teaching approaches conducted in this course emphasize analysis, modeling and design principles of AI methodology. Case studies, engineering applications of AI in data science analytics, will also be covered in this course. Collaboration Policy: You are free to form study groups and discuss programming assignments and projects. However, you must write up assignments and code independently, and you must acknowledge in your submission all the students you discussed with.

Course Schedule 本課程將「科技英文寫作」融入於「深度學習」教學（中文授課、英文教材）。課程係以學習「深度學習」專業知識技術為主，而以學習「科技英文」英文知識為輔！！ Week Topic (PART I) 1 課程介紹 2 八大詞性與五大句型 3 連接詞與關係代名詞 (PART II) 4 Foundations for AI 5 Supervised learning (I) -- Multi-Layer Perceptron (MLP) (classification problems) (scikit-learn) 6 Supervised learning (II) -- Multi-Layer Perceptron (MLP) (non-linear regression/function approximation problems) (Programming assignment #1) 7 Supervised learning (III) -- MLP (TensorFlow/Keras) 8 Programming assignment #1 (Due / Oral presentation) **9 Mid-term paper reading report (Due / Oral presentation) 10 Supervised learning (IV) -- MLP (time-series data modeling and forecasting) (scikit-learn/TensorFlow) 11 Unsupervised learning -- k-Means clustering (scikit-learn) (PART III) 12 Supervised learning (V) -- MLP as Deep Learning models (TensorFlow (GPU accelerated)) 13 Programming assignment #2 (Due / Oral presentation) 14 Supervised learning (VI) -- Convolutional Neural Networks (CNNs)/Long Short-Term Memory (LSTM) (TensorFlow/Keras (GPU accelerated)) (Programming assignment #2) 15 Supervised/Structured learning (I) -- Generative Adversarial Networks (Generative AI) 16 Programming assignment #2 (Due / Oral presentation) 17 Supervised/Structured learning (II) -- Multi-Task Learning/Transfer Learning **18 Final paper reading report (Due / Oral presentation)

Evaluation and grading policy - 學習總成績: 85起算 - Assignments (PPT only): 至多 +5 (Optional) (Two or Three programming assignments: No assignment turned in late will be accepted) - Mid-term paper reading report (PPT only): 至多 +5 (Optional) - Final paper reading report (PPT only): 至多 +5 (Optional)

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Course lectures will be distributed through our University's iStudy+ (e-learning) system.

The access to curricular consultation e-mail: yhlin@ntut.edu.tw
LINE : this course's LINE group (will be announced)

The course corresponds to the SDGs SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
SDG11：永續城市與社區（Sustainable Cities and Communities）

Does the course incorporate AI ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）
● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）

Note
　

Instructor	Yu-Hsiu Lin　Office Hours
Email	yhlin@ntut.edu.tw
Last Updated	2025-12-18 17:20:08
Course Objective	This semester's Deep Learning (DL) course will focus on Machine Learning (ML), DL, and Generative AI in data science analytics. We will look at supervised, unsupervised and structured learning methods, which will include classic Multi-Layer Perceptron (MLP) as a shallow or Deep Neural Network (DNN), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), k-Means clustering, and Generative Adversarial Networks (GANs). This course is designed for graduate students who are interested in AI and its applications in data science analytics. Students who take this course should have mathematical maturity of undergraduate curricula in science and engineering, including Calculus, Discrete Mathematics, Linear Algebra and programming language such as C/C++/MATLAB/Python. Teaching approaches conducted in this course emphasize analysis, modeling and design principles of AI methodology. Case studies, engineering applications of AI in data science analytics, will also be covered in this course. Collaboration Policy: You are free to form study groups and discuss programming assignments and projects. However, you must write up assignments and code independently, and you must acknowledge in your submission all the students you discussed with.
Course Schedule	本課程將「科技英文寫作」融入於「深度學習」教學（中文授課、英文教材）。課程係以學習「深度學習」專業知識技術為主，而以學習「科技英文」英文知識為輔！！ Week Topic (PART I) 1 課程介紹 2 八大詞性與五大句型 3 連接詞與關係代名詞 (PART II) 4 Foundations for AI 5 Supervised learning (I) -- Multi-Layer Perceptron (MLP) (classification problems) (scikit-learn) 6 Supervised learning (II) -- Multi-Layer Perceptron (MLP) (non-linear regression/function approximation problems) (Programming assignment #1) 7 Supervised learning (III) -- MLP (TensorFlow/Keras) 8 Programming assignment #1 (Due / Oral presentation) 9 Mid-term paper reading report (Due / Oral presentation) 10 Supervised learning (IV) -- MLP (time-series data modeling and forecasting) (scikit-learn/TensorFlow) 11 Unsupervised learning -- k-Means clustering (scikit-learn) (PART III) 12 Supervised learning (V) -- MLP as Deep Learning models (TensorFlow (GPU accelerated)) 13 Programming assignment #2 (Due / Oral presentation) 14 Supervised learning (VI) -- Convolutional Neural Networks (CNNs)/Long Short-Term Memory (LSTM) (TensorFlow/Keras (GPU accelerated)) (Programming assignment #2) 15 Supervised/Structured learning (I) -- Generative Adversarial Networks (Generative AI) 16 Programming assignment #2 (Due / Oral presentation) 17 Supervised/Structured learning (II) -- Multi-Task Learning/Transfer Learning 18 Final paper reading report (Due / Oral presentation)
Evaluation and grading policy	- 學習總成績: 85起算 - Assignments (PPT only): 至多 +5 (Optional) (Two or Three programming assignments: No assignment turned in late will be accepted) - Mid-term paper reading report (PPT only): 至多 +5 (Optional) - Final paper reading report (PPT only): 至多 +5 (Optional)
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Course lectures will be distributed through our University's iStudy+ (e-learning) system.
The access to curricular consultation	e-mail: yhlin@ntut.edu.tw LINE : this course's LINE group (will be announced)
The course corresponds to the SDGs	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure） SDG11：永續城市與社區（Sustainable Cities and Communities）
Does the course incorporate AI	● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
Note