50
Assessment Methods
Continuous Assessment: 50%
Final Examination: 50%
WID3006
MACHINE LEARNING
Credit:
3
Course Pre-requisite(s):
None
Medium of Instruction:
English
Learning Outcomes
1. Explain the concepts and techniques for
supervised learning, semi-supervised learning
and unsupervised learning.
2. Use the appropriate machine learning
techniques for given sample datasets.
3. Apply practical solutions to solve common
problems in machine learning.
Synopsis of Course Content
This course covers a broad understanding of the
field of machine learning and statistical pattern
recognition. Topics include classification and linear
regression, Bayesian network, decision trees,
SVMs, statistical learning method, unsupervised
learning and reinforcement learning.
Assessment Methods
Continuous Assessment: 50%
Final Examination: 50%
WID3007
FUZZY LOGIC
Credit:
3
Course Pre-requisite(s):
None
Medium of Instruction:
English
Learning Outcomes
1. Identify the concept of fuzzy logics.
2. Determine the operations, inferences and
relation in fuzzy logics.
3. Apply fuzzy logic techniques and concepts in
various problems.
Synopsis of Course Content
This course explores how principles from theories of
fuzzy logic can be used to construct machines in
real-world of uncertainty. In particular, the course
covers techniques from fuzzification, defuzzification,
fuzzy operator and fuzzy type-II in solving real-world
problems.
Assessment Method
Continuous Assessment: 50%
Final Examination: 50%
WID3008
IMAGE PROCESSING
Credit:
3
Course Pre-requisite(s) :
None
Medium of Instruction:
English
Learning Outcomes
1. Develop knowledge of image processing
techniques and methodologies.
2. Explain various image processing methods and
algorithms for particular classes of problems.
3. Apply various image processing methods and
algorithms in variety of open-ended design
problems.
Synopsis of Course Content
This course explores how principles from theories of
image processing can be used to construct
machines that exhibit nontrivial behavior. In
particular, the course covers techniques from
geometry, computer vision, machine learning and
image processing in solving real-world problems.
Assessment Methods
Continuous Assessment: 50%
Final Examination: 50%
WID3009
ARTIFICIAL INTELLIGENCE GAME
PROGRAMMING
Credit:
3
Course Pre-requisite(s):
WID3004 Numerical Methods
Medium of Instruction:
English
Learning Outcomes
1. Apply the laws of physics in programing
simulations.
2. Write programs that represent a state-space
and elements within it.
3. Use Artificial Intelligence techniques in game
programs.
Synopsis of Course Content
This course covers applications of Artificial
Intelligence (AI) in games and their implementation
of these techniques. Using AI in games allows
students to créate interactive games that are
entertaining and challenging. This course includes
topics such as reasoning, tracking player behavior,
movement and animation.
Assessment Methods
Continuous Assessment: 50%
Final Examination: 50%
