At the end of this course, the students; 1) Know hypotheses and version spaces. 2) Know how to design a learning system. 3) Know how to apply machine learning techniques to specific problems.
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
None
COURSE DEFINITION
Machine learning paradigms. Concept learning, general-to-specific ordering and version spaces. Decision tree learning. Artificial neural networks, perceptrons, and multilayer networks. Bayesian learning. Instance-based learning. Support vector machine. Genetic algorithms. Applications to selected problems.
COURSE CONTENTS
WEEK
TOPICS
1st Week
Machine learning paradigms.
2nd Week
Concept learning, general-to-specific ordering and version spaces.
3rd Week
Concept learning, general-to-specific ordering and version spaces.
4th Week
Decision tree learning.
5th Week
Decision tree learning.
6th Week
Artificial neural networks, perceptrons, and multilayer networks.
7th Week
Artificial neural networks, perceptrons, and multilayer networks.
8th Week
Mid-term
9th Week
Bayesian learning.
10th Week
Instance-based learning.
11th Week
Support vector machine.
12th Week
Genetic algorithms.
13th Week
Genetic algorithms.
14th Week
Applications to selected problems.
RECOMENDED OR REQUIRED READING
1. Machine Learning, Tom Mitchell, McGraw-Hill. 2. Pattern Recognition and Machine Learning, Christopher Bishop, Springer. 3. Pattern Classification, Richard O. Duda, Wiley.