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.
Venue: LKC_LT1 (3/F) . See classroom_info and cuhk-campus-map.
Lab:
FYB_222.
Assignments: can be found at Blackboard
Materials
to be taught by Dr. 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_seq2seq.pptx |
|
10_neural_object_detect.pptx |
|
11_adaboost.pptx | https://scikit-learn.org/stable/ |
12_decision_tree.pptx |
Course Schedule
2024
Week |
Date |
Topic |
1 |
11
Jan |
Introduction
to Decision Making and Decision Support** · Principles of
Decision Making · Paradigms for
Decision Making · Fundamentals of
Decision Support |
2 |
18
Jan |
Introduction
to Smart-City design # · Hardware systems · Software Systems · Case studies |
3 |
25
Jan |
Symbolic
Approaches to Spatial Knowledge Representation and
Inference # · Basic
Principles · Paradigms · Basic
Artificial Intelligence Techniques |
4 |
1
Feb |
Fuzzy
Logic # · Methods and
Examples |
5 |
8
Feb |
Computer
vision methods for smart cities # · Basic Image
Processing Algorithms · Application
examples |
15 Feb |
No
lecture, Chinese New Year Holiday |
|
6 |
22
Feb |
Modern
Neural Network Models and Applications # ·
Feedforward design and back propagation methods |
7 |
29
Feb |
Convolutional
Neural Network (CNN) # · Design and
application |
8 |
7
Mar |
Recursive
Neural Network (RNN) # · Design and
application |
9 |
14
Mar |
Natural
Language Processing # · Sequence to
Sequence Neural Models · Word
Embedding and Applications ·
Introduction to Transformer, ChatGPT and Large Language
Model (LLM) |
10 |
21
Mar |
Neural
object detection methods # · Object
recognition models · Object
localization models ·
Segmentation models |
11 |
28
Mar |
Ensemble
methods for machine learning # · AdaBoost
method
|
4 Apr |
Ching Ming Festival (no
lecture) |
|
12 |
11 Apr |
Data classification using
decision tree # ·
Gini Index, Information Entropy methods ·
Overfitting problem and solution |
13 |
18 Apr |
Fully Integrated SDSS Laboratory
problem solving in the Implementation and Application of
the Fully Integrated SDSS: GRS |
14 |
25
Apr |
Exam:
Covering materials taught by Dr. KH Wong # |
**Lectures to be taught by Prof. Y.
Leung # Lectures to be taught by Dr. KH Wong
Final Exam:
Date:
See the table above . Venue: to be announced.
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. The exam will cover and include all materials
taught in the lectures. We will
provide each student an answer book, supplementary sheets and
scrap paper for calculation. 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.
Sample exam paper.
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 Dr. K.H. Wong (3 assignments, 10% each)
·
Exam 60%: Covering materials taught by Dr.
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
·
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.