NCHU Course Outline
Course Name (中) 高等人工智慧(6656)
(Eng.) Advanced Artificial Intelligence
Offering Dept Department of Computer Science and Engineering
Course Type Elective Credits 3 Teacher Yi-Chung Chen
Department Department of Computer Science and Engineering/Graduate Language English Semester 2025-SPRING
Course Description This course will be taught by Prof. Yi-Chung Chen. He will give lectures about artificial intelligence language, methods and applications. Topics will cover Data Mining Techniques, Genetic Algorithms, Neural Networks and Deep Learning.
Prerequisites
self-directed learning in the course N
Relevance of Course Objectives and Core Learning Outcomes(%) Teaching and Assessment Methods for Course Objectives
Course Objectives Competency Indicators Ratio(%) Teaching Methods Assessment Methods
By taking this class, students acquire the basic artificial intelligence knowledge and have the ability to apply it in practical cases.
1.Having abilities on computer science literacy, information theory, and mathematical analysis.
6.Having abilities of self-learning, communicating and coordinating team work.
8.Respecting academic ethics and having the abilities of presenting and writing academic research paper.
50
30
20
topic Discussion/Production
Exercises
Discussion
Practicum
Other
Lecturing
Written Presentation
Oral Presentation
Assignment
Quiz
Course Content and Homework/Schedule/Tests Schedule
Week Course Content
Week 1 Overview of Artificial Intelligence Concepts
Week 2 The concept of neural networks I
Week 3 The concept of neural networks II
Week 4 Neural Network Toolbox Implementation
Week 5 The concept of deep Learning models I
Week 6 The concept of deep Learning models II
Week 7 The concept of deep Learning models III
Week 8 Real case implementation
Week 9 Midterm Exam
Week 10 The concept of data mining algorithms I
Week 11 The concept of data mining algorithms II
Week 12 Data mining Toolbox Implementation
Week 13 The concept of genetic algorithms I
Week 14 The concept of genetic algorithms II
Week 15 Final Exam
Week 16 Self-directed learning: Final project I
Week 17 Self-directed learning: Final project II
Week 18 Final project report
Evaluation
Homework 30%
Exam 40%
Final project: 30%

Final project 25%
Textbook & other References
Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learning. MIT Press.
Teaching Aids & Teacher's Website
Please download the handouts from the ilearning 3.0 website
Office Hours
Please make an appointment directly with the teacher
Sustainable Development Goals, SDGs
include experience courses:N
Please respect the intellectual property rights and use the materials legally.Please repsect gender equality.
Update Date, year/month/day:2025/01/18 16:01:55 Printed Date, year/month/day:2025 / 1 / 22
The second-hand book website:http://www.myub.com.tw/