Course Syllabus

Course Information：

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

Spring Semester
Year 2024 328170 Deep Learning 1 3.0 3 ★ YAO LEEHTER GEE
IEECSG
SSRT
GEECS
MSAI 49 5 電機所、電資AI、創新AI、電資外生所和智慧鐵道班合開

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2024	328170	Deep Learning	1	3.0	3	★	YAO LEEHTER	GEE IEECSG SSRT GEECS MSAI	49	5	電機所、電資AI、創新AI、電資外生所和智慧鐵道班合開

Course Plan：

Instructor YAO LEEHTER　Office Hours

Email ltyao@ntut.edu.tw

Last Updated 2024-05-27 11:06:59

Course Objective 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 (1 wk) 2. Shallow Neural Networks (2 wks) 3. Training Deep Neural Networks (2 wks) 4. Teaching Deep Learners to Generalize (3 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 exams. (30%, 35%, 35%)

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 You are welcome to email me with the following email address:
ltyao@ntut.edu.tw

The course corresponds to the SDGs

Note
SDG 4 and 8.

After final exam, you are required to learn Deep Reinforcement Learning by reading the tutorial provided in the i-school. Moreover, the second part of final take-home exam. will be conducted from June 9th to 17th. Please carefully read the provided tutorial and watch the uploaded recorded video in i-school.

Instructor	YAO LEEHTER　Office Hours
Email	ltyao@ntut.edu.tw
Last Updated	2024-05-27 11:06:59
Course Objective	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 (1 wk) 2. Shallow Neural Networks (2 wks) 3. Training Deep Neural Networks (2 wks) 4. Teaching Deep Learners to Generalize (3 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 exams. (30%, 35%, 35%)
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	You are welcome to email me with the following email address: ltyao@ntut.edu.tw
The course corresponds to the SDGs
Note	SDG 4 and 8. After final exam, you are required to learn Deep Reinforcement Learning by reading the tutorial provided in the i-school. Moreover, the second part of final take-home exam. will be conducted from June 9th to 17th. Please carefully read the provided tutorial and watch the uploaded recorded video in i-school.