EE4285 Computational Intelligence
The courses provides an introduction to fundamental theory and concepts of computational intelligence methods, specifically, fuzzy systems, neural networks, genetic algorithms and their applications in the area of machine intelligence.

Lecture 1: Introduction to Fuzzy Sets
A fuzzy set is totally characterised by a Membership function (MF). And is mathematically expressed as $$S = {(x, \mu_s(x)  x\in X)}$$ The above equation is known as characteristic equation.

Lecture 2: Fuzzy Relations & Compositions
Fuzzy Relations Binary relation: When there are two universe of discourse $X \times Y$ , then a fuzzy relation is defined as $$R = {((x, y), \mu_{R}(x,y)) (x,y) \in X \times Y}$$

Lecture 3: Fuzzy Ifthen & Fuzzy Reasoning
Fuzzy Ifthen rule Fuzzy Ifthen rules basically encodes fuzzy relations. If $x$ is $A$, then $y$ is $B$. In $A$ and $B$ are linguistic values defined by fuzzy sets on universes of discourse $X$ and $Y$.

Lecture 4: Fuzzy Inference System
General definition: A computing framework based on the concepts of fuzzy set theory, fuzzy ifthen rules, and fuzzy reasoning. Interpretation: Specifically, it is a framework used to model and infer the behaviour of a system.
References
 Dr. Su Rong. 2022. “EE4285: Computational Intelligence,” Singapore: Nanyang Technological University.