Information Package / Course Catalogue
Artifical Intelligence
Course Code: KBU113
Course Type: Area Elective
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 4
Objectives of the Course

Implementing different Artificial Intelligence methodologies, coding artificial intelligence methods and types in different programming languages. Modeling problems in different fields with artificial intelligence techniques.

Course Content

Artificial Intelligence Definition, History and Basic Concepts, Artificial Intelligence Types and Application Areas Classification Problems and Probabilistic Classification (Simple Bayes) Classification Problems and Example-Based Classification (k-nn, decision trees) Clustering Algorithms Heuristic Search Algorithms Genetic Algorithm Heuristic Search Algorithms Genetic Algorithm, Artificial Bee Colony Algorithm, Symbiosis Algorithm Prediction Problems and Algorithms Artificial Neural Networks, Prediction Problems and Algorithms Artificial Neural Networks, Heuristic Prediction Algorithm Coding of Artificial Neural Networks and Application to Engineering Problems

Name of Lecturer(s)
Learning Outcomes
1.They learn the Basic Concepts of Artificial Intelligence
2.They learn Artificial Intelligence Types and Application Areas
3.They Learn Supervised Learning Methods
4.They Learn Unsupervised Learning Methods
5.They Learn Reinforcement Learning Methods
Recommended or Required Reading
1.Artificial Intelligence Applications in Engineering, Ufuk Bookstore, August 2003.
2.Artificial Neural Networks, Çetin ELMAS, Seçkin Publications, Ankara, 2003
Weekly Detailed Course Contents
Week 1 - Theoretical
Artificial Intelligence Definition, History and Basic Concepts
Week 2 - Theoretical
Types of Artificial Intelligence and Application Areas
Week 3 - Theoretical
Classification Problems and Probabilistic Classification (Naive Bayes)
Week 4 - Theoretical
Classification Problems and Example Based Classification (k-nn, decision trees)
Week 5 - Theoretical
Clustering Algorithms
Week 6 - Theoretical
Heuristic Search Algorithms Genetic Algorithm
Week 7 - Theoretical
Heuristic Search Algorithms Artificial Bee Colony Algorithm, Symbiosis Search Algorithm
Week 8 - Theoretical
Prediction Problems and Algorithms Artificial Neural Networks
Week 9 - Theoretical
Heuristic Prediction Algorithms
Week 10 - Intermediate Exam
Midterm Exam
Week 11 - Theoretical
Coding of a Heuristic Estimation Algorithm and Its Application to Engineering Problems
Week 12 - Theoretical
Coding Artifical Neural Networks
Week 13 - Theoretical
Deep Neural Networks
Week 14 - Theoretical
Deep Neural Networks
Week 15 - Final Exam
Final Exam
Assessment Methods and Criteria
Type of AssessmentCountPercent
Assignment1%5
Quiz1%5
Midterm Examination1%30
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142256
Assignment25010
Midterm Examination117017
Final Examination117017
TOTAL WORKLOAD (hours)100
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
OÇ-1
5
5
OÇ-2
5
5
OÇ-3
5
5
OÇ-4
5
5
OÇ-5
5
5
Adnan Menderes University - Information Package / Course Catalogue
2026