NCHU Course Outline
Course Name (中) 人工智慧解決智慧城市問題的系統方法(7503)
(Eng.) A System Approach of Problem-Solving Using AI for Smart Cities
Offering Dept Engineering
Course Type Elective Credits 1 Teacher Undefined
Department Engineering Language English Semester 2024-FALL
Course Description This course consists of four parts: 1) present AI history, concept, and models, 2) present a system approach of problem-solving using AI, 3) present and discuss existing AI applications, including their use, benefits, performance matrix and annotation data for training and validation, and 4) present complete solutions of AI applications. This course combines conventional teaching and case studies.
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
Students are able to
 Use a system approach to effectively review and summarize the key points of the AI applications and articles.
 Describe AI history, concepts, and models.
 Summarize and discuss existing AI applications, including their use, benefits, performance matrix, annotation data for training and validation, and complete solutions.
topic Discussion/Production
Lecturing
Oral Presentation
Quiz
Course Content and Homework/Schedule/Tests Schedule
Week Course Content
Week 1 1st Course (Monday - July 8, 2024): AI history, concepts, and models
Week 2 2nd Course (Tuesday - July 9, 2024): A system approach of problem-solving using AI for smart cities
Week 3 3rd Course (Wednesday - July 10, 2024): Annotation, performance measures models (Evaluation: Mid-term exam)
Week 4 4th Course (Thursday - July 11, 2024): AI applications, including their use, benefits, performance matrix, annotation data for training and validation, and complete solutions; complete solutions for Smart Cities & case studies (Group Assignment)
Week 5 5th Course (Friday - July 12, 2024): Case studies (Evaluation: Group Presentation)
Week 6
Week 7
Week 8
Week 9
Week 10
Week 11
Week 12
Week 13
Week 14
Week 15
Week 16
Week 17
Week 18
Evaluation
Assessment:
 Group Assignment (Case Study)
 Mid-term exam
Textbook & other References
Learning material will include the following:
1) Open web sites, including but not limited to the following https://www.sas.com/en_us/insights/analytics/machine-learning.html
https://quantdare.com/machine-learning-a-brief-breakdown/
https://www.edureka.co/blog/ai-vs-machine-learning-vs-deep-learning/
2) Published papers & reports
a)Tsai, Y., Zhao, Y., Pop-Stefanov, B., Chatterjee, A. C. (2020) “Automatically Detect and Classify Asphalt Pavement Raveling Severity Using 3D Technology and Machine Learning”, International Journal of Pavement Research and Technology, pp. 1-9.
b)Hsieh, Y., Tsai, Y. (2020) “Machine Learning for Crack Detection: review and model performance comparison”, ASCE Journal of Computing in Civil Engineering, 34 (5), 04020038.
c)Hsieh, Y. A., Yang, Z., and Tsai, Y. C. (2021). Convolutional neural network for automated classification of jointed plain concrete pavement conditions. Computer‐Aided Civil and Infrastructure Engineering, 36(11), 1382-1397.
d)Bukharin, A. W., Yang, Z., Tsai, Y. (2021). "Five-Year Project-Level Statewide Pavement Performance Forecasting Using a Two-Stage Machine Learning Approach Based on Long Short-Term Memory." Transportation Research Record 2675.11 (2021): 280-290.
e)Georgia Department of Transportation Pavement Condition Evaluation System Manual, 2017.
f)Florida Department of Transportation Flexible Pavement Condition Survey Handbook, 2017.
g) Tsai, Y., Wu, Y., Six, N., Pranav, C. (2019) “Curve Safety Improvements Using Mobile Device and Automatic Curve Sign Detection – Phase I”, RP 18-18, Final Report, Georgia Department of Transportation.
h)Tsai, Y., Yu, P., Liu, T., Knezevich R., Wang, C. (2021) “Curve Safety Improvements Using Mobile Device and Automatic Curve Sign Detection – Phase II”, RP 19-26, Final Report, Georgia Department of Transportation.
3) Power point presentations, introducing basic concepts, models, and different real-world AI applications
Teaching Aids & Teacher's Website
https://ce.gatech.edu/people/faculty/1001/overview (CV has provided since the website publication has not yet been updated by GT IT)
Office Hours
TBA
Sustainable Development Goals, SDGs(Link URL)
include experience courses:N
Please respect the intellectual property rights and use the materials legally.Please respect gender equality.
Update Date, year/month/day:2024/12/05 08:54:16 Printed Date, year/month/day:2025 / 7 / 04
The second-hand book website:http://www.myub.com.tw/