This course introduces students to the different algorithms presented in the
field of Machine Learning with emphasis on its deigning, modelling and
programming. The real world systems are complex, the complexity arises
from uncertainty in the form of imprecision/vagueness/ambiguity/
fuzziness. Modelling these systems and expressing them using the
traditional algorithmic approaches are not always possible. This course
explores the essential theory behind designing, developing and
programming systems that demonstrate intelligent behaviors, learning from
experience and mimicking nature behaviors to represent real world systems
and to consider uncertainties.
