ARTIFICIAL INTELLIGENCE

Program Type:

Thesis

Course Code:

CSE562

Course Type:

Area Elective

P:

3

Lab:

0

Credits:

3

ECTS:

10

Course Language:

English

Course Objectives:

The aim of this course is to provide students the knowledege about the basic techniques and methodologies of artificial intelligence and abilities to apply artificial intelligence methods on practical problems.

Course Content:

Basic concepts and techniques of AI. Problem solving in AI, informed and uninformed search techniques, Local search techniques and simulated annealing.. Meta-heuristic search methods. Introducion to Neural Networks. Game playing, Prolog overview, knowledge representation and reasoning.

Teaching Methods:

Teaching Methods: 1: Lecture, 2: Discussion, 3: Seminar, 4: Research, 5: Simulation/Case Study/Role Playing, 6: Problem Session, 7: Invited Lecturer

Assessment Methods:

Assessment Methods: A: Exam, B: Assignment, C: Presentation, D: Research, E: Debate, F: Quiz, G: Participation

Vertical Tabs

Course Learning Outcomes

Course Learning Outcomes	Program Learning Outcomes	Teaching Methods	Assessment Methods
Knolwedege about the basic methodologies in artificial intelligence.	3	1	A,B,B
Ability to use knowledge to formulate, and solve practical problems using artificial intelligence techniques.	9,10	4,6	A,D

Course Flow

COURSE CONTENT
Week	Topics	Study Materials
1	Introductory terms, foundations, history and philosophy of AI	Textbook
2	Intelligent Agents	Textbook
3	Problem Solving and Introduction to Search Methods	Textbook
4	Uninformed Search Methodologies	Textbook
5	Heuristic Search	Textbook
6	Game Playing	Textbook
7	Meta-Heuristics	Textbook
8	Neural Networks	Textbook
9	Knowledge Based Agents	Textbook
10	First Order Logic	Textbook
11	Inference in First Order Logic	Textbook
12	Prolog and Logic Programming	Textbook
13	Prolog and Logic Programming	Textbook
14	Probabilistic Reasoning	Textbook

Recommended Sources

RECOMMENDED SOURCES
Textbook	Artificial Intelligence: A Modern Approach. Stuart Russell, Peter Norvig, Prentice Hall, Second Edition
Additional Resources

Assessment

ASSESSMENT
IN-TERM STUDIES	NUMBER	PERCENTAGE
Mid-terms	1	50
Assignment	4	50
Total		100
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE		40
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE		60
Total		100

Course’s Contribution to Program

COURSE'S CONTRIBUTION TO PROGRAM
No	Program Learning Outcomes	Contribution
No	Program Learning Outcomes	1	2	3	4	5
1	Learning about empirical findings and theoretical perpectives in Cognitive Science.
2	Approaching findings, methods, opinions, and theories in Cognitive Science critically and multi-directionally.
3	Learning about research methods in Cognitive Science.			X
4	Searching the literature and reading, compehending, summarizing, and synthesizing contemporary articles in Cognitive Science.
5	Forming original research questions in Cognitive Science.
6	Relying on and converging findings from different disciplines in Cognitive Science in the process of forming a research question.
7	Conducting all steps of research in Cognitive Science.
8	Conducting research and applications ethically.
9	Using contemporary information technologies for following contemporary research and innovations.				X
10	Understanding that learning is necessary throughout the lifespan, and obtaining the skills to realize that.				X

ECTS

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Course Duration (Excluding the exam week: 13x Total course hours)	13	3	39
Hours for off-the-classroom study (Pre-study, practice)	14	5	70
Midterm examination	1	2	2
Homework	4	35	140
Final examination	1	3	3
Total Work Load			254
Total Work Load / 25 (h)			10.16
ECTS Credit of the Course			10

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