Information Package / Course Catalogue
Artificial Intelligence and Applications in the Automotive Industry
Course Code: OTE001
Course Type: Area Elective
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 1
Prt.: 1
Credit: 2
Lab: 0
ECTS: 3
Objectives of the Course

Artificial intelligence and optimal design are two of the most important concepts used to compete in the automotive industry. This course aims to equip students with the ability to understand heuristic algorithms and artificial intelligence in the design of automotive parts, to solve engineering problems using heuristic algorithms, and to present a heuristic algorithm project.

Course Content

This course primarily focuses on the application of artificial intelligence methods in design. The aim is to equip graduate students in automotive engineering with the ability to learn and actively utilize advanced AI and experimental design methods.

Name of Lecturer(s)
Learning Outcomes
1.They are knowledgeable about artificial intelligence and its applications.
2.They understand artificial intelligence methods.
3.They learn advanced numerical analysis and optimization techniques and apply them to automotive engineering problems.
4.They understand the concept of optimum design.
5.They can use artificial intelligence in the automotive industry.
Recommended or Required Reading
1.Artificial Intelligence Optimization Algorithms, Derviş Karaboğa, Nobel Publishing, 2017.
Weekly Detailed Course Contents
Week 1 - Theoretical
Lesson plan explanations and expected study methods from students.
Week 2 - Theoretical
Genetic algorithms
Week 3 - Theoretical & Practice
Genetic algorithms and application examples.
Week 4 - Theoretical
Particle swarm algorithm
Week 5 - Theoretical & Practice
Examples of particle swarm algorithm applications
Week 6 - Theoretical
Differential evolution algorithm
Week 7 - Theoretical & Practice
Examples of applying differential evolution algorithms.
Week 8 - Intermediate Exam
Exam
Week 9 - Theoretical
Artificial immune system algorithms
Week 10 - Theoretical & Practice
Artificial immune system algorithms and application examples.
Week 11 - Theoretical
Bee colony algorithm
Week 12 - Theoretical & Practice
Bee colony algorithm application examples
Week 13 - Theoretical & Practice
Applications for the automotive industry
Week 14 - Theoretical & Practice
Applications for the automotive industry
Week 15 - Practice
Project presentations
Week 16 - Final Exam
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 - Theory120224
Lecture - Practice100110
Assignment301030
Project20510
Quiz1011
Midterm Examination1011
Final Examination1011
TOTAL WORKLOAD (hours)77
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
PÇ-13
OÇ-1
3
4
1
1
2
2
2
OÇ-2
4
4
1
2
2
2
3
OÇ-3
5
4
3
4
2
3
5
3
2
OÇ-4
4
3
1
3
2
1
4
2
1
OÇ-5
5
5
4
4
2
3
4
3
2
Adnan Menderes University - Information Package / Course Catalogue
2026