Course Syllabus

Course Information：

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

Spring Semester
Year 2025 341988 Deep Learning 1 3.0 3 ★ YAO LEEHTER GEE
IEECSG
MSAI 79 6 Course jointly offered with Electrical Engineering Dept.

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2025	341988	Deep Learning	1	3.0	3	★	YAO LEEHTER	GEE IEECSG MSAI	79	6	Course jointly offered with Electrical Engineering Dept.

Course Plan：

Instructor YAO LEEHTER　Office Hours

Email ltyao@ntut.edu.tw

Last Updated 2024-12-18 21:19:50

Course Objective 1. 類神經網路簡介 2. 淺層類神經網路 3. 深層類神經網路之訓練 4. 深度學習與推論 5. 遞歸類神經網路 6. 長短期記憶類神經網路 7. 卷積類神經網路 8. 深度強化學習 1. Introduction to Neural Networks 2. Shallow Neural Networks 3. Training Deep Neural Networks 4. Teaching Deep Learners to Generalize 5. Recurrent Neural Networks 6. Long Short-term Memory Neural Networks 7. Convolutional Neural Networks 8. Deep Reinforcement Learning

Course Schedule 1. Introduction to Neural Networks (2 Wks) 2. Shallow Neural Networks (2 Wks) 3. Training Deep Neural Networks (2 Wks) 4. Teaching Deep Learners to Generalize (2 Wks) 5. Recurrent Neural Networks (2 Wks) 6. Long Short-term Memory Neural Networks (2 Wks) 7. Convolutional Neural Networks (3 Wks) 8. Deep Reinforcement Learning (3 Wks)

Evaluation and grading policy Three take-home programming exam. First exam: 30% Second exam: 30% Final exam: 40%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Neural Networks and Deep Learning, Charu C. Aggarwal, Springer, 2018.

The access to curricular consultation 18:00-20:00, Monday to Wednesday.

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

Note
　

Instructor	YAO LEEHTER　Office Hours
Email	ltyao@ntut.edu.tw
Last Updated	2024-12-18 21:19:50
Course Objective	1. 類神經網路簡介 2. 淺層類神經網路 3. 深層類神經網路之訓練 4. 深度學習與推論 5. 遞歸類神經網路 6. 長短期記憶類神經網路 7. 卷積類神經網路 8. 深度強化學習 1. Introduction to Neural Networks 2. Shallow Neural Networks 3. Training Deep Neural Networks 4. Teaching Deep Learners to Generalize 5. Recurrent Neural Networks 6. Long Short-term Memory Neural Networks 7. Convolutional Neural Networks 8. Deep Reinforcement Learning
Course Schedule	1. Introduction to Neural Networks (2 Wks) 2. Shallow Neural Networks (2 Wks) 3. Training Deep Neural Networks (2 Wks) 4. Teaching Deep Learners to Generalize (2 Wks) 5. Recurrent Neural Networks (2 Wks) 6. Long Short-term Memory Neural Networks (2 Wks) 7. Convolutional Neural Networks (3 Wks) 8. Deep Reinforcement Learning (3 Wks)
Evaluation and grading policy	Three take-home programming exam. First exam: 30% Second exam: 30% Final exam: 40%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Neural Networks and Deep Learning, Charu C. Aggarwal, Springer, 2018.
The access to curricular consultation	18:00-20:00, Monday to Wednesday.
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
Note