Computational Intelligence.

Catalog description

Computational intelligence is broadly defined as the concepts, models, and algorithms inspired by intelligent biological systems. Students will apply computational intelligence paradigms and techniques to real world data sets and optimization problems. Topics include types of learning, theory of generalization, linear and logistic regression, non-linear transformation, fundamentals of neural networks, evolutionary computation and optimization, fuzzy set theory and fuzzy logic, and other current topics in computational intelligence. Familiarity with basics in linear algebra, probability, and analysis of algorithms recommended. Prerequisites: Graduate or blended students in the School of Engineering and Computer Science.