Download Subject Description Form Subject Code EIE426 Subject Title

Subject Description Form
Subject Code
EIE426
Subject Title
Artificial Intelligence and Computer Vision
Credit Value
3
Level
4
Pre-requisite/
Co-requisite/
Exclusion
Nil
Objectives
1. To introduce the student the major ideas, methods, and techniques of
Artificial Intelligence (AI) and computer vision;
2. To develop an appreciation for various issues in the design of intelligent
systems; and
3. To provide the student with programming experience from implementing AI
techniques, simple knowledge systems, and computer vision applications.
Intended Subject
Learning
Outcomes
Upon completion of the subject, students will be able to:
Category A: Professional/academic knowledge and skills
1. Understand the benefits and limitations of current AI techniques, its culture
and society impacts, and possible future development.
2. Implement major game search techniques for simple computer games.
3. Apply machine learning techniques to information processing and data mining.
4. Develop simple knowledge systems for internet and engineering applications.
5. Explore robotics and computer vision techniques, and their applications to
entertainment and engineering domains.
Category B: Attributes for all-roundedness
6. Communicate effectively, and present ideas and findings clearly in oral and
written forms.
7. Think critically and creatively.
8. Demonstrate self-learning and life-long learning capability.
9. Work in a team and collaborate effectively with others.
10. Recognize social responsibility and ethics.
Contribution of
the Subject to the
Attainment of the
Programme
Outcomes
Programme Outcomes:
Category A: Professional/academic knowledge and skills
 Programme Outcomes 1, 3, and 5: This subject contributes to the
programme outcomes through teaching of the theories and concepts of
artificial intelligence and computer vision and through providing the students
with an opportunity to apply their knowledge.
 Programme Outcomes 2, 3, and 4: This subject contributes to the
programme outcomes by providing the students with laboratory exercises to
simulate search techniques, to construct simple knowledge systems using a
knowledge system shell, and to apply machine learning techniques to
practical problems.
 Programme Outcomes 2, 3, 4, 5, and 7: This subject contributes to the
programme outcomes through a group mini-project to develop a simple
intelligence system.
 Programme Outcomes 2 and 7: This subject contributes to the programme
outcomes through teaching of useful tools for building artificial intelligence
and computer vision systems.
Category B: Attributes for all-roundedness
 Programme Outcome 10: This subject contributes to the programme
outcome by providing students with an opportunity to think critically and
creatively about various artificial intelligence and computer vision
methodologies.
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Subject Synopsis/
Indicative
Syllabus
Programme Outcome 11: This subject contributes to the programme
outcome by providing students with the foundations for life-long learning and
continual professional development in the areas of artificial intelligence and
computer vision.
Programme Outcomes 9, 10, 11, and 12: This subject contributes to the
programme outcomes by providing students with an opportunity to work as a
team in developing a simple intelligence system.
Programme Outcome 13: This subject contributes to the programme
outcome through a discussion on the culture and society impacts of artificial
intelligence techniques.
Syllabus:
1. Introduction
The Definitions and Foundations of AI, the History of AI, and the State of the
Art.
2. Intelligent Agents
Agents and Environments, the Concept of Rationality, the Nature of
Environments, the Structure of Agents, Applications.
3. Blind and Informed Search Methods
Problem-Solving Agents, Example Problems, Searching for Solutions,
Uninformed Search Strategies, Avoiding Repeated States, Searching with
Partial Information, Informed (Heuristic) Search Strategies, Heuristic
Functions, Local Search Algorithms and Optimization Problems, Local
Search in Continuous Spaces, Online Search Agents and Unknown
Environments.
4. Game Playing
Games, Optimal Decisions in Games, Alpha-Beta Pruning, Imperfect
Decisions, Games That Include an Element of Chance, State-of-the-Art
Game Programs.
5. Knowledge Systems
Rule-Based Deduction Systems, Rule-Based Reaction Systems, Forward
and Backward Chaining, the Knowledge Engineering Process, Analysis of
Typical Knowledge Systems.
6. Machine Learning
Forms of Learning, Inductive Learning, Learning Decision Trees,
Computational Learning Theory, Machine Learning Techniques for
Intelligent Information Processing and Data Mining.
7. Computer Vision
Imaging and Representation, Image Preprocessing, Extracting 3-D
Information, Object Recognition, Using Vision for Manipulation and
Navigation, Eye Tracking Techniques and Applications, Concepts of Virtual
Reality, Applications.
8. Robotics
Robot Hardware, Robotic Perception, Planning to Move, Planning Uncertain
Movements, Robotic Software Architectures, Entertainment Robots,
Engineering Applications.
9. Culture and Society Impacts
Understanding Intelligence: Issues and Directions, the Ethics and Risks of
Developing Artificial Intelligence Solutions.
Laboratory Experiments:
1. Search methods and machine game playing
2. Eye tracking applications
Teaching/
Learning
Methodology
Teaching and
Learning
Method
Intended
Subject
Learning
Outcome
Remarks
Lectures
1, 2, 3, 4, 5,
10
fundamental principles and key concepts of
the subject are delivered to students;
guidance on further readings, applications
and implementation is given.
Tutorials
1, 2, 3, 4, 5,
7, 10
supplementary to lectures and
conducted with smaller class size;
students will be able to clarify concepts
to have a deeper understanding of
lecture material;
problems and application examples
given and discussed.
Laboratory
sessions
2, 5, 6, 7, 8
are
and
the
are
students will develop simple computer
games using software tools;
students will collect eye tracking data using
an eye tracker and analyze the collected
data.
Mini-project
1, 2, 3, 4, 5,
6, 7, 8, 9
students in a group of 3-4 are required to
work on an intelligent system chosen by the
group.
Alignment of
Assessment and
Intended Subject
Learning
Outcomes
Specific
Assessment
Methods/Tasks
%
Weighting
1. Continuous
Assessment

Tests

Laboratory
Intended Subject Learning Outcomes to
be Assessed (Please tick as
appropriate)
1
2
3
4
5
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6
7
8
9
10
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45%
Report

Mini-project:
Type A
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Demo,
Type B
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Presentation,
Type C
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Report
Type D
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55%
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2. Examination
Total
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100 %
Explanation of the appropriateness of the assessment methods in
assessing the intended learning outcomes:
Specific Assessment
Methods/Tasks
Remark
Tests and examination
end-of chapter type problems used to evaluate
students’ ability in applying concepts and skills
learnt in the classroom;
students need to think critically and creatively in
order to come with an alternate solution for an
existing problem.
Lab report
Each student is required to produce a written
report;
accuracy and the presentation of the report will be
assessed.
each group of students are required to produce a
written report;
accuracy and the presentation of the report will be
assessed;
the functions and performance of the developed
intelligent system will be assessed in the demo
session;
a presentation session will be arranged to assess
technical knowledge and communication skills of
each group member.
Mini-project
Student Study
Effort Expected
Class contact (time-tabled):

Lecture
24 Hours

Tutorial/Laboratory/Practice Classes
18 hours
Other student study effort:

Lecture: preview/review of notes;
homework/assignment; preparation for
test/quizzes/examination
36 Hours

Tutorial/Laboratory/Practice Classes: preview of
27 Hours
materials, revision and/or reports writing
Total student study effort:
Reading List and
References
105 Hours
Reference Books:
1. G.F. Luger, Artificial Intelligence: Structures and Strategies for Complex
Problem Solving, 6th ed., Pearson Education, 2009.
2. L.G. Shapiro and G. Stockman, Computer Vision, Prentice-Hall, 2001.
3. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 2nd ed.,
Prentice-Hall, 2003.
4. A. Duchowski, Eye Tracking Methodology: Theory and Practice, 2nd ed.,
Springer-Verlag, 2007.
5. P.H. Winston, Artificial Intelligence, 3rd ed., Addison-Wesley, 1992.