PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
CENG 417	ARTIFICIAL INTELLIGENCE	3 + 0	5th Semester	5

COURSE DESCRIPTION

Course Level	Bachelor's Degree
Course Type	Elective
Course Objective	This subject aims to give students an introduction to the field of Artificial Intelligence, covering the basic techniques and mechanisms for AI programming. The students completing this subject will have understanding of the historical and conceptual development of AI, the goals of AI and the methods employed to achieve them, the social and economic roles of AI and also have the skills to analyze problems and determine where AI techniques are applicable, implement AI problem-solving techniques.
Course Content	Introduction to artificial intelligence, Natural and Artificial Intelligence, Turing Test, Searching Methods, Planning, Heuristic Problem Solving, Knowledge representation, Predicate Logic, AI programming languages, Programming in Common Lisp, Game Theory, Genetic Algorithms, Expert Systems, Artificial Intelligence Applications.
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	Learning artificial intelligence concepts
2	Understanding the application areas
3	Enhancing the subject by studying on a sample project

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	PO 12
LO 001							5	5	5	4	3	5
LO 002							5	5	5	5	5	5
LO 003							4	4	4	4	4	4
Sub Total							14	14	14	13	12	14
Contribution	0	0	0	0	0	0	5	5	5	4	4	5

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	3	42
Assignments	2	10	20
Mid-terms	1	10	10
Final examination	1	16	16
Total Work Load ECTS Credit of the Course			130 5

COURSE DETAILS

Select Year

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
CENG 417	ARTIFICIAL INTELLIGENCE	3 + 0	1	Turkish	2017-2018 Fall

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Prof. Dr. SEZAİ TOKAT	stokat@pau.edu.tr		Course location is not specified.	%

Goals		This subject aims to give students an introduction to the field of Artificial Intelligence, covering the basic techniques and mechanisms for AI programming. The students completing this subject will have understanding of the historical and conceptual development of AI, the goals of AI and the methods employed to achieve them, the social and economic roles of AI and also have the skills to analyze problems and determine where AI techniques are applicable, implement AI problem-solving techniques.
Content		Introduction to artificial intelligence, Natural and Artificial Intelligence, Turing Test, Searching Methods, Planning, Heuristic Problem Solving, Knowledge representation, Predicate Logic, AI programming languages, Programming in Common Lisp, Game Theory, Genetic Algorithms, Expert Systems, Artificial Intelligence Applications.

Topics

Weeks	Topics
1	Definitions of Artificial Intelligence. Turing Test. Chinese Room Test.
2	Heuristic Problem Solving. NP Problems. A* Algorithm. Heuristic Problem examples.
3	Greedy Best First Search, A* Search, Hill Climbing, Simulated Annealing Algorithms. Heuristic Problem examples.
4	Games, Game Theory.
5	Simplex Method, Minimax Method, Alfa-Beta Pruning.
6	Predicate Logic.
7	Logical Programming.
8	Genetic algorithms
9	Midterm Examination Week.
10	Artificial Neural Networks
11	Artificial Neural Networks
12	Artificial Neural Networks
13	Fuzzy Logic.
14	Fuzzy Logic.

Materials

Materials are not specified.

Resources

Resources	Resources Language
Vasif Vagifoğlu Nabiyev, Yapay Zeka, 5. baskı, Nisan 2016, Seçkin Yayıncılık.	Türkçe
S. RAJASEKARAN,G.A. VIJAYALAKSHMI PAI, Neural Networks, Fuzzy Systems and Evolutioary Algorithms, 2. baskı, PHI Learning, Delhi, 2017.	English
Stuart Russell, ‎Peter Norvig, Artificial Intelligence: A Modern Approach, Global Edition, Pearson, 2016.	English

Course Assessment

Assesment Methods	Percentage (%)	Assesment Methods Title
Final Exam	60	Final Exam
Midterm Exam	40	Midterm Exam

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

Prospective Student