PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
ELK 525	COMPUTATIONAL INTELLIGENCE	3 + 0	2nd Semester	7,5

COURSE DESCRIPTION

Course Level	Doctorate Degree
Course Type	Elective
Course Objective	Introducing concepts, models, algorithms, and tools for development of intelligent systems. Example topics include artificial neural networks, genetic algorithms, fuzzy systems, swarm intelligence, ant colony optimization, artificial life, and hybridizations of the above techniques.
Course Content	Artificial Neural Networks, Support Vector Machines, Fuzzy Systems, Evolutionary Computation Algorithms.
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	An understanding of the fundamental Computational Intelligence models
2	Implemented neural networks, genetic algorithms, fuzzy neural networks, and ant colony optimization algorithms.
3	Applied Computational Intelligence techniques to classification, pattern recognition, prediction, rule extraction, and optimization problems.

COURSE'S CONTRIBUTION TO PROGRAM

	PO 01	PO 02	PO 03	PO 04	PO 05	PO 06	PO 07	PO 08	PO 09	PO 10	PO 11
LO 001	2	5	4	4	4
LO 002	2	5	4	4	4
LO 003	2	4	5	5	5
Sub Total	6	14	13	13	13
Contribution	2	5	4	4	4	0	0	0	0	0	0

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities	Quantity	Duration (Hour)	Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)	14	3	42
Hours for off-the-classroom study (Pre-study, practice)	14	5	70
Mid-terms	1	40	40
Final examination	1	43	43
Total Work Load ECTS Credit of the Course			195 7,5

COURSE DETAILS

Select Year

This course is not available in selected semester.

L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes

Prospective Student