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WID2001
KNOWLEDGE REPRESENTATION AND
REASONING
Credit:
3
Course Pre-requisite(s):
None
Medium of Instruction:
English
Learning Outcomes
1. Describe different kinds of knowledge and their
related engineering processes.
2. Explain the various knowledge representation
and knowledge reasoning methods.
3. Use the various knowledge representation and
knowledge reasoning methods to solve
problems.
Synopsis of Course Content
This course describes the different kinds of
knowledge and their related engineering processes.
It explains the various knowledge representations
methods such as rule-based, frame-based, case-
based reasoning, semantic network, script,
conceptual graph and ontology. It also explains the
various knowledge reasoning methods such as the
deductive, inductive, monotonic and non-monotonic
reasoning. Students will use the various knowledge
representation and knowledge reasoning methods
to solve problems.
Assessment Methods
Continuous Assessment: 50%
Final Examination: 50%
WID2002
COMPUTING MATHEMATICS II
Credit:
3
Course Pre-requisite(s):
None
Medium of Instruction:
English
Learning Outcomes
1. Apply various formulae for operations on
differentiation and integration as well as various
matrix algebra.
2. Solve problems involving various types of
mathematical transformations.
3. Apply statistical methods and sampling in
problem solving.
Synopsis of Course Content
This course covers important mathematics topics
which can be applied to artificial intelligence field.
The topics include calculus (differentiation and
integration), functions and graphs, matrix algebra
(Eigen value, Eigen vector, dependency,
singularity), statistical methods (sampling, principle
component analysis) and transformations (Fourier,
Laplace, Hough, geometric and wavelet).
Assessment Methods
Continuous Assessment: 50%
Final Examination: 50%
WID2003
COGNITIVE SCIENCE
Credit:
3
Course Pre-requisite(s):
None
Medium of Instruction:
English
Learning Outcomes
1. Identify various concepts and processes of
cognition.
2. Explain the functions of memory in relation to
learning activities.
3. Apply cognitive theories in solving problems in
everyday life.
Synopsis of Course Content
This course covers the fundamentals on cognitive
science. It covers topics on mind and machine,
perception (object recognition), attention &
consciousness, memory (short term memory,
working memory and long term memory), forgetting,
mental representation and visual perception,
category, language, intelligence and creativity,
emotion and expression, problem solving, reasoning
and decision making.
Assessment Methods
Continuous Assessment: 50%
Final Examination: 50%
WID3001
FUNCTIONAL AND LOGIC PROGRAMMING
Credit:
3
Course Pre-requisite(s):
None
Medium of Instruction:
English
Learning Outcomes
1. Describe basic principles and features of
functional and logic programming.
2. Explain concepts and methods of functional
and logic programming.
3. Apply functional and logic programming
knowledge.
Synopsis of Course Content
This course introduces Artificial Intelligence (AI)
programming languages, which covers functional
and logic styles of programming. It describes the
functional programming that uses functions as its
basis and includes topics such as types and
classes, lists, recursions, and higher-order
functions. The logic programming is based on
formal logic and includes topics such as clauses
and predicates, unification, operators and
arithmetic, cuts and negation.
