Ders Bilgi Formu (İngilizce) Course Name: Artificial Intelligence

Ders Bilgi Formu (İngilizce)
Course Name:
Artificial Intelligence
Program
Information Systems Engineering
Credit:
6
Year- Semester:
4/7
Hours/Credit:
Instructor(s):
bilisimsistem@mu.edu.tr
T 2
U 2
L 0
C 6
Course Code:
BSM 4511
Level of Course:
Undergraduate
Required/Elective:
Elective
Language:
Turkish
Teaching Methods: Teaching, Demonstration
Course Objectives: This course aims to introduce the basic concepts of Artificial Intelligence. In addition, the current technologies
enabling Artificial Intelligence are discussed.
Course Content: Introduction to and history of artificial intelligence, agents, intelligent agents, problem solving, A* search and
heuristic functions, uninformed, local and online search, constraint satisfaction, game playing, logical agents, propositional logic and
inference, first order logic and inference, logic programming, planning problems
I. Week
Introduction to and History of Artifical Intelligence
II. Week
Agents
III. Week
Intelligent Agents
IV. Week
Problem Solving, Uninformed Search
V. Week
A* Search and Heuristic Functions, Local Search
VI. Week
Online Search, Constraint Satisfaction
VII. Week
Constraint Satisfaction and Game Playing
VIII. Week
Midterm
IX. Week
Logical Agents
X. Week
Propositional Logic, Inference in Propositional Logic
XI. Week
First Order Logic
XII. Week
Inference in First Order Logic
XIII. Week
Logic Programming
XIV. Week
Planning Problems
Anticipated Learning Outcomes:

Be able to develop a variety of approaches with general applicability

Be able to understand Artificial Intelligence search models and generic search strategies

By using Bayesian networks, be able to use the probability as a mechanism for handling uncertainty in AI

Be able to explore the design of Artificial Intelligence systems that use learning to improve their performance on a given
task

Be able to present logic as a formalism for representing knowledge in AI systems

Be able to address specific domains such as computer vision, natural language processing, and robotics
Assessment Method(s): Midterm Exam (30%), Final Exam (40%), Assignments (30%)
Textbook: Yapay Zeka, Problemler-Yöntemler-Algoritmalar, Vasif V. Nabiyev, Seçkin Yayıncılık, 3. Basım, Ankara, 2010.
Recommended Reading: Yapay Zeka Uygulamaları, Çetin Elmas, Seçkin Yayıncılık, İstanbul, 2011.
Pre/Co-requisites: None