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 355724 Artificial Intelligence Programming (II) 1 2.0 3 ★ TSAI,I-HAUR 4ERACE1 39 0 計網電腦教室

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	355724	Artificial Intelligence Programming (II)	1	2.0	3	★	TSAI,I-HAUR	4ERACE1	39	0	計網電腦教室

Course Plan：

Instructor TSAI,I-HAUR　Office Hours

Email iht@ntut.edu.tw

Last Updated 2025-12-15 12:45:32

Course Objective 本課程為「程式設計與人工智慧（一）」之進階課程，著重於 Python 程式設計的延伸應用，並導入機器學習與深度學習之核心概念。課程以 TensorFlow 與 Keras 為主要工具，透過實作多層感知器（MLP）、卷積神經網路（CNN）及循環神經網路（RNN），使學生具備人工智慧模型建構、訓練、評估與應用的能力。 This course is a continuation of Programming and Artificial Intelligence (I). It focuses on advanced Python-based programming techniques and introduces students to machine learning and deep learning concepts. Using TensorFlow and Keras, students will learn to design, train, evaluate, and deploy neural network models, including Multilayer Perceptrons (MLP), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), through hands-on practice and real-world datasets.

Course Schedule 1. Course Introduction 2. Overview of Artificial Intelligence, Machine Learning, and Deep Learning 3. Introduction to TensorFlow and Keras 4. Setting Up the Deep Learning Development Environment 5. GPU Acceleration and CUDA Configuration 6. Midterm Exam I 7. Fundamentals of Multilayer Perceptrons (MLP) 8. Data Preprocessing and Feature Engineering 9. MLP Implementation: Handwritten Digit Recognition 10. Model Evaluation and Overfitting Prevention 11. Fundamentals of Convolutional Neural Networks (CNN) 12. CNN Implementation: Image Classification 13. Fundamentals of Recurrent Neural Networks (RNN) 14. Midterm Exam II 15. RNN Implementation: Time-Series Prediction 16. Model Saving and Loading 17. Model Deployment and Practical Applications 18. Final Exam ※ Course schedule and content are subject to adjustment based on students’ learning progress.

Evaluation and grading policy Midterm Exam I: 30% Midterm Exam II: 30% Final Exam: 40% ※ Evaluation items and grading weights are subject to adjustment based on students’ learning progress.

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:No
參考書籍: Python零基礎入門班碁峰圖書精通 Python｜運用簡單的套件進行現代運算碁峰圖書機器學習與深度學習特訓班碁峰圖書

The access to curricular consultation email：iht@ntut.edu.tw

The course corresponds to the SDGs 無（None）

Does the course incorporate AI ● 無（None）

Note
　

Instructor	TSAI,I-HAUR　Office Hours
Email	iht@ntut.edu.tw
Last Updated	2025-12-15 12:45:32
Course Objective	本課程為「程式設計與人工智慧（一）」之進階課程，著重於 Python 程式設計的延伸應用，並導入機器學習與深度學習之核心概念。課程以 TensorFlow 與 Keras 為主要工具，透過實作多層感知器（MLP）、卷積神經網路（CNN）及循環神經網路（RNN），使學生具備人工智慧模型建構、訓練、評估與應用的能力。 This course is a continuation of Programming and Artificial Intelligence (I). It focuses on advanced Python-based programming techniques and introduces students to machine learning and deep learning concepts. Using TensorFlow and Keras, students will learn to design, train, evaluate, and deploy neural network models, including Multilayer Perceptrons (MLP), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), through hands-on practice and real-world datasets.
Course Schedule	1. Course Introduction 2. Overview of Artificial Intelligence, Machine Learning, and Deep Learning 3. Introduction to TensorFlow and Keras 4. Setting Up the Deep Learning Development Environment 5. GPU Acceleration and CUDA Configuration 6. Midterm Exam I 7. Fundamentals of Multilayer Perceptrons (MLP) 8. Data Preprocessing and Feature Engineering 9. MLP Implementation: Handwritten Digit Recognition 10. Model Evaluation and Overfitting Prevention 11. Fundamentals of Convolutional Neural Networks (CNN) 12. CNN Implementation: Image Classification 13. Fundamentals of Recurrent Neural Networks (RNN) 14. Midterm Exam II 15. RNN Implementation: Time-Series Prediction 16. Model Saving and Loading 17. Model Deployment and Practical Applications 18. Final Exam ※ Course schedule and content are subject to adjustment based on students’ learning progress.
Evaluation and grading policy	Midterm Exam I: 30% Midterm Exam II: 30% Final Exam: 40% ※ Evaluation items and grading weights are subject to adjustment based on students’ learning progress.
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No 參考書籍: Python零基礎入門班碁峰圖書精通 Python｜運用簡單的套件進行現代運算碁峰圖書機器學習與深度學習特訓班碁峰圖書
The access to curricular consultation	email：iht@ntut.edu.tw
The course corresponds to the SDGs	無（None）
Does the course incorporate AI	● 無（None）
Note