Information Package / Course Catalogue
Artificial Intelligence Applications II
Course Code: YZO201
Course Type: Required
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 1
Credit: 4
Lab: 0
ECTS: 4
Objectives of the Course

Implementing various Artificial Intelligence (AI) methodologies, coding AI methods and types in different programming languages, and modeling problems in different fields with AI techniques.

Course Content

Artificial Intelligence Types and Application Areas Classification Problems and Probabilistic Classification (Naive 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 Their Application to Engineering Problems

Name of Lecturer(s)
Learning Outcomes
1.They learn about Artificial Intelligence Types and Application Areas
2.They Learn Supervised Learning Methods
3.They Learn Unsupervised Learning Methods
4.They Learn Reinforcement Learning Methods
5.They can develop hybrid AI algorithms
Recommended or Required Reading
1.Artificial Intelligence: Foundations of Computational Agents, David Poole, Alan Mackworth, Cambridge University Press 2010.
Weekly Detailed Course Contents
Week 1 - Theoretical
Types of Artificial Intelligence and Application Areas
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 (Midterm Exam)
Week 9 - Theoretical
Prediction Problems and Algorithms Artificial Neural Networks, Heuristic Prediction Algorithm
Week 10 - Theoretical
Coding a Heuristic Estimation Algorithm and Its Application to Engineering Problems
Week 11 - Theoretical
Coding a Heuristic Estimation Algorithm and Its Application to Engineering Problems
Week 12 - Theoretical
Coding Artificial Neural Networks and Their Application to Engineering Problems
Week 13 - Theoretical
Coding Artificial Neural Networks and Their Application to Engineering Problems
Week 14 - Theoretical
Deep Neural Networks
Assessment Methods and Criteria
Type of AssessmentCountPercent
Assignment1%10
Quiz1%10
Midterm Examination1%20
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140342
Lecture - Practice140114
Assignment110111
Quiz110111
Midterm Examination110111
Final Examination110111
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
PÇ-10
PÇ-11
PÇ-12
OÇ-1
4
4
4
5
4
5
4
5
4
4
4
5
OÇ-2
5
5
4
5
4
5
4
5
4
4
4
5
OÇ-3
5
5
4
5
4
5
4
5
4
5
4
5
OÇ-4
5
5
4
5
5
4
5
4
5
4
5
5
OÇ-5
4
5
4
5
5
5
4
5
4
5
5
4
Adnan Menderes University - Information Package / Course Catalogue
2026