Dokuz Eylül University Information Package / Courses Catalog

Description of Individual Course Units

Course Unit Code	Course Unit Title	Type Of Course	D	U	L	ECTS
CSC 5033	INTRODUCTION TO INTELLIGENT SYSTEMS	ELECTIVE	3	0	0	8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

ASSISTANT PROFESSOR BARIŞ TEKIN TEZEL

Offered to

Ph.D. in Computer Science (English)
Computer Science
Artificial Intelligence and Intelligent Systems

Course Objective

To learn at least five basic components of IS, To be able to design and implement own IS, To write an IS based research paper in one subfield, To have both a general breadth knowledge of AI techniques, plus a deeper specialized knowledge of one particular sub-area within AI; how to combine or integrate them.

Learning Outcomes of the Course Unit

1	To be able to design and implement own IS system.
2	To do research in state-of-the-art subjects of Intelligent Systems area; preparing and doing presentation.
3	To learn ability to use and integrate Software Tools in Artificial Neural Networks, Genetic Algorithms, Fuzzy Logic, Expert Systems.
4	To learn basic concepts of Intelligent Systems, mathematical and software background;
5	To have ability to apply Intelligent Systems to problems.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week	Subject	Description
1	Introduction to Intelligent Systems (IS)
2	Hybrid Intelligent Systems, Neural Expert Systems, Neuro-Fuzzy Systems, Hybrid Intelligent System Architectures and ANFIS
3	Expert Systems, Rule-based Expert Systems, Uncertainty Management (Bayesian Reasoning, Certainty Factors)
4	Evolutionary Computation, Swarm Intelligence Algorithms
5	Machine Learning, Deep Learning, Fuzzy Logic
6	Fuzzy Expert Systems, Frame-Based Expert Systems, Knowledge Engineering and Data Mining Robotics Applications
7	Intelligent Systems In Business, Recommender Systems,
8	Natural Language Processing, Sentiment Analysis, Ensemble Learning
9	Hybrid Metaheuristics, Hyperheuristics
10	Intelligent agents
11	Overall evaluation
12	Project evaluations
13	Project evaluations
14	Project evaluations

Recomended or Required Reading

Textbook(s): Michael Negnevitsky, Artificial Intelligence : A Guide to Intelligent Systems (3rd Edition) , Addison Wesley, 2011.
Supplementary Book(s):
Doç. Dr. Bahadır Karasulu, "Esnek Hesaplama", Nobel, 2015.
Mircea Negoita, Daniel Neagu, Vasile Palade, Computational Intelligence: Engineering of Hybrid Systems (Studies in Fuzziness and Soft Computing) , Springer, 2005.
Computational Intelligence: A Logical Approach. Poole, Mackworth and Goebel. Oxford University Press, 1998.
Neuro-Fuzzy and Soft Computing. J.S.R. Jang, C.T. Sun, E.Mizutani. Prentice Hall 1997.
Recent literature papers.

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation and discussion format. Besides the taught lecture, group presentations are to be prepared by the groups assigned and presented in a discussion session. In some weeks of the course, results of the homework given previously are discussed.

Assessment Methods

SORTING NUMBER	SHORT CODE	LONG CODE	FORMULA
1	MTE	MIDTERM EXAM
2	ASG	ASSIGNMENT
3	FCG	FINAL COURSE GRADE	MTE * 0.50 + ASG * 0.50

*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

efendi.nasibov@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities	Number	Time (hours)	Total Work Load (hours)
Lectures	14	3	42
Preparations before/after weekly lectures	14	4	56
Preparing assignments	2	35	70
Preparing presentations	2	15	30
TOTAL WORKLOAD (hours)			198

Contribution of Learning Outcomes to Programme Outcomes

PO/LO	PO.1	PO.2	PO.3	PO.4	PO.5
LO.1	5	5	5	5	5
LO.2	5	5	5	5	5
LO.3	5	5	5	5	5
LO.4	5	5	5	5	5
LO.5	5	5	5	5	5

COURSE UNIT TITLE

DEGREE PROGRAMMES

Description of Individual Course Units

Offered By

Level of Course Unit

Course Coordinator

Offered to

Course Objective

Learning Outcomes of the Course Unit

Mode of Delivery

Prerequisites and Co-requisites

Recomended Optional Programme Components

Course Contents

Recomended or Required Reading

Planned Learning Activities and Teaching Methods

Assessment Methods

Further Notes About Assessment Methods

Assessment Criteria

Language of Instruction

Course Policies and Rules

Contact Details for the Lecturer(s)

Office Hours

Work Placement(s)

Workload Calculation

Contribution of Learning Outcomes to Programme Outcomes

CONTACT INFORMATION