Introduction to Soft Computing

The course provides an in-depth overview of the theoretical and the practical aspects of the soft computing paradigm. The main focus is on the theory and application of probabilistic graphical models (commonly known as Bayesian networks in the Artificial Intelligence community) and related topics, such as, knowledge elicitation issues, belief updating in singly and multiply connected networks, simulation schemes for belief updating, parameter and structure learning of Bayesian networks, and integration of time and uncertainty. Alternative models of uncertain reasoning (including belief function theory and fuzzy logic) and biologically inspired computational models (neural networks and evolutionary algorithms) are also presented.

Hours: XYZ where X = Lecture, Y = Lab, Z = Credit
All hours are per week.
3 Lab hours constitute 1 credit hour
1 credit hour implies 1 lecture of 50mins per academic week. 16 weeks in total.
Pre-Requisite courses are courses required to be completed before this course may be taken
Co-Requisite courses are courses required to be taken along with this course