BlackboardCourse code and title : GISM5033 Artificial Intelligence for Smart Cities
Program: MSc in GeoInformation Science and Smart Cities
Web based support : https://blackboard.cuhk.edu.hk  or  https://portal.cuhk.edu.hk
Updated: 30 June 2025

Year: 2025-26 Term2

GISM5033 Course outline:

Blackboard Web based support : https://blackboard.cuhk.edu.hk

This course aims at the concepts and applications of artificial intelligence in solving real-life problems in Smart-City management. Fundamentals of artificial intelligence methods for decision making will be discussed. It covers various paradigms for artificial-intelligence applications in spatial knowledge representation and inference, computational models and intelligent spatial decision support systems (SDSS). The enrichment of spatial decision support by data mining and knowledge discovery will also be examined. Open-source artificial intelligence software systems will be introduced to facilitate discussions, and to assist students to acquire hands-on experience of the relevant knowledge.


Course Professor of this part :Prof. Kin Hong Wong     ( khwongCSE)

Venue: TBA. See  classroom_info and cuhk-campus-map.  Lab: FYB_222.  Teaching time table

 
Assignments: can be found at Blackboard

Materials to be taught by Prof. KH Wong # are shown below.
Tools for assignments: MATLAB online , COLAB,  Colab_tutorial,  keras_examples , SKLEARN,  anaconda(python system), opencv installation guides

Lecture Notes (may update from time to time to improve the quality)
       Additional information / program code
                                                                                                                                                             
02_smartcities.pptx
 Public domain software.pptx,  https://colab.research.google.com/ ,   https://keras.io/examples/
03_symbolic.pptx
04_fuzzy.pptx
05_vision.pptx
 Octave (Free Scientific Programming Tool similar to MATLAB), Octave Guide
06_neural_net.pptx
 cnn_mnist.py, 
07_cnn.pptx
08_rnn.pptx
09_nlp.pptx
10_transformer_llm.pptx
11_neural_object_detect.pptx
12_adaboost.pptx   https://scikit-learn.org/stable/
13_decision_tree.pptx
14_reinforcement_learning

CTE

Testing images

Course Schedule

2026

Week

Date
(Monday 6:30 pm)

Topics

1

5 Jan

Introduction to Decision Making and Decision Support**

·      Principles of Decision Making

·      Paradigms for Decision Making

·      Fundamentals of Decision Support

2

12 Jan

Introduction to Smart-City Design #

Symbolic Approaches to Spatial Knowledge Representation and Inference #

·       Basic Principles

·       Paradigms

·       Basic Artificial Intelligence Techniques

3

19 Jan

Fuzzy Inference Systems #

·       Methods and Examples

4

26 Jan

Computer Vision Methods for Smart Cities #

·       Image Processing Algorithms

5

2 Feb

Machine Learning Methods #

·        Neural Network models

·        Feedforward Design and Back Propagation Methods

6

9 Feb

Convolutional Neural Network (CNN) #

·        Design and Applications

16 Feb No lecture, Chinese New Year Holiday

7

23 Feb

Recurrent Neural Network (RNN) #

·      Design and Applications

8

2 Mar

Natural Language Processing (NLP) #

·     Sequence to Sequence Neural Models

·     Word Embedding and Applications

·        Introduction to Transformer, ChatGPT and Large Language Model (LLM)

9

9 Mar

Neural object detection methods #

·     Object Recognition and Localization Models

10

16 Mar

Ensemble methods for machine learning #   

·     AdaBoost method                       

11

23 Mar

Data classification using decision tree #

·     Gini Index, Information Entropy methods

·    Overfitting problem and solution

Reinforcement Learning

12

30 Mar

Digital Twins of Cities **

·        Concept of Digital Twins

·        Fundamentals of Digital Twins of Cities

·        AI and Digital Twins of Cities

6 Apr

 No class:  Tomb sweeping Day

13

13 Apr

Fully Integrated SDSS (Lab Venue: see top of this web-page)

Laboratory problem solving in the Implementation and Application of the Fully Integrated SDSS: GRS

14

20 Apr

Exam: Covering materials taught by Prof. KH Wong #

The exam is from 6:45 pm (Please come to the exam venue at 6:30pm).

Venue: TBA. See  classroom_info and cuhk-campus-map.

**Lectures to be taught by Prof. Y. Leung.   # Lectures to be taught by Prof. KH Wong

Advanced Topics:


Final Exam:

Date/venue: See the teaching time table above.
The exam will last for 2 hours, it is a closed-book exam and no cheat sheet allowed. However, the GISM5033_formulas.docx  will be printed and attached to your exam question paper.  We will provide each student an answer book, supplementary sheets and scrap paper for calculation. Materials in the appendices of lecture notes will not be included in the exam. A non-programmable calculator can be used. We accept calculators that are (or similar to that) in the list https://www.hkeaa.edu.hk/en/IPE/hkia/index.html. Please read the exam rules, and sample_exam_paper.docx


gism5033.5_std_list.ppt.zip

Expected Learning Outcome

The emphasis of this course is placed on the fundamentals of artificial intelligence methods for developing decision support systems and the hands-on experience in using and designing

decision support systems for smart-city management. After taking the course, students are expected to:

- Understand the basic concepts of artificial intelligence methods;

- Know the basic paradigms for knowledge representation and inference based on artificial intelligence methods;

- Use in-house spatial decision support system (SDSS) tool to develop simple SDSS.

 

Learning Activities

There will be lectures and laboratory works in this course. Lectures emphasize concepts, systems, and applications. Laboratory works center on the acquisition of know-hows in the use of in-house development tools to build SDSS for spatial decision making. CUHK Blackboard will be used to facilitate the dissemination of teaching and learning materials as well as course management.

 

Assessment Scheme

·         Assignments 30%: Covering materials taught by Prof. K.H. Wong (3 assignments, 10% each)

·         Exam 60%: Covering materials taught by Prof. K.H. Wong


·      Lab 10%: Covering Laboratory assignments

Feedback for Evaluation

In order to improve the teaching and learning quality for this course, the following feedback mechanisms are implemented.

Feedback

To whom

Where

When

Qualitative feedback from students/discussion forums

Tutors and/or teachers through informal interaction

During lecture and outside class

Throughout the term

Course evaluation

Teachers and department

Lecture room

End of the term

Visiting examiner report

University, department and teachers

Overseas

End of the term

Reflection of teachers (including evidence from assessment)

Teachers and tutors

All learning activities

Throughout the term

Curriculum review

Related teachers and Curriculum & Teaching Committee

Department

End of the term

 

References

·   R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introductions, 2nd ed. The MIT Press, 2018

·   Theodoridis, Sergios. Machine learning: a Bayesian and optimization perspective. Academic press, 2015.

·   Pandit, Abhijit. Mathematical Modeling using Fuzzy Logic: Applications to Sustainability. Chapman and Hall/CRC, 2021.

·   Arbib, M.A. (2nd ed.). The Handbook of Brain Theory and Neural Networks. Cambridge: MIT, 2003.

·   Giarratano, J. and G. Riley. Expert Systems: Principles and Programming. Boston: PWS-KENT, 1998.

·   Goodfellow I., Y. Bengio and A. Courville. Deep Learning. Cambridge: MIT Press, 2016.

·   Han, J. and M. Kamber. Data Mining: Concepts and Techniques. San Francisco: Morgan Kaufmann Publishers, 2001.

·   Jackson, P. Introduction to Expert Systems. Reading: Addison-Wesley, 1990.

·   Leung, Y. Intelligent Spatial Decision Support Systems. Berlin: Springer-Verlag. 1997.

·   Leung, Y. Knowledge Discovery in Spatial Data. Berlin: Springer-Verlag. 2010.

·   Miller, H.J. and J. Han (eds.). Geographic Data Mining and Knowledge Discovery. London: Taylor & Francis, 2001.

·   Negotia, C. V. Expert Systems and Fuzzy Systems. California: Benjamin/Cummings, 1985.

·   Rashid, Tariq. Make your own neural network. CreateSpace Independent Publishing Platform, 2016.

·   Rich, E. and K. Knight. Artificial Intelligence. New York: McGraw-Hill, 1991.

·   Sergios Theodoridis. Machine Learning : A Bayesian and Optimization Perspective 2nd edition, 2020, Academic press

·   Turban, E. Decision Support and Expert Systems: Management Support Systems. New York: Macmillan, 1993.

·   Turban, E. Expert Systems and Applied Artificial Intelligence. N.Y.: Macmillan, 1992.

·   Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems 30 (2017).

·   Ray, Partha Pratim. "ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope." Internet of Things and Cyber-Physical Systems (2023).

·   Kasneci, Enkelejda, et al. "ChatGPT for good? On opportunities and challenges of large language models for education." Learning and individual differences 103 (2023): 102274.


Advanced topics (Not included in the exam)