Information Package / Course Catalogue
Use of Artificial Intelligence Tools and Platforms
Course Code: YZO106
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: 4
Objectives of the Course

To provide a fundamental understanding of the nature and characteristics of Artificial Intelligence and its applications, and to explain the concepts, methods, and technologies used for Artificial Intelligence and its applications. To familiarize yourself with Artificial Intelligence tools and platforms.

Course Content

Data Literacy and AI analytics, AI use cases for organizations and society, xAI, using Excel spreadsheets in AI-powered decision support, AI Privacy, Ethics, and Security, Optimization Applications and Approaches in AI, Mathematics of Machine Learning, Introduction to Artificial Neural Networks, Text mining and AI, Language models and word semantics in AI, Emerging AI applications in social media and networks, AI platforms and their uses

Name of Lecturer(s)
Ins. Ümit BULUT
Learning Outcomes
1.To recognize the concepts and applications of Artificial Intelligence.
2.To have knowledge about the approaches, methods and tools underlying Artificial Intelligence.
3.Understanding the implications of Artificial Intelligence.
4.Understanding the evolution of Artificial Intelligence.
5.To be knowledgeable about contemporary tools and applications of Artificial Intelligence.
Recommended or Required Reading
1.Özkan, M. (2021). Introduction to Machine Learning and Applications. Kodlab Publications.
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to Artificial Intelligence Data Literacy
Week 2 - Theoretical
Analytical Questions with Artificial Intelligence, Prompt Engineering
Week 3 - Theoretical
Optimization Approaches in Artificial Intelligence
Week 4 - Theoretical
Applications of Optimization in Artificial Intelligence
Week 5 - Theoretical
The Mathematics of Machine Learning
Week 6 - Theoretical
Introduction to Artificial Neural Networks
Week 7 - Theoretical
Text mining and artificial intelligence
Week 8 - Theoretical
Language models, Word semantics and relations (Midterm exam)
Week 9 - Theoretical
Artificial intelligence applications emerging in social media and networks
Week 10 - Theoretical
Hugging Face platform
Week 11 - Theoretical
TensorFlow platform
Week 12 - Theoretical
OpenAI API
Week 13 - Theoretical
Google AI
Week 14 - Theoretical
The world's artificial intelligence ecosystem and manufacturers using artificial intelligence
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142142
Lecture - Practice142142
Project1224
Midterm Examination1516
Final Examination1516
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
5
5
5
5
5
4
5
4
5
4
5
4
OÇ-2
4
5
4
5
4
5
4
5
4
5
4
5
OÇ-3
3
5
5
4
5
4
5
5
5
5
5
3
OÇ-4
5
5
5
4
4
4
5
4
5
4
5
5
OÇ-5
5
5
5
5
4
5
4
5
4
5
4
5
Adnan Menderes University - Information Package / Course Catalogue
2026