Information Package / Course Catalogue
Professional Artificial İntelligence and İts Applications
Course Code: TAK231
Course Type: Area Elective
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 1
Prt.: 2
Credit: 2
Lab: 0
ECTS: 3
Objectives of the Course

This course aims to introduce the fundamental concepts and methods of artificial intelligence; to develop an understanding of machine learning, deep learning, natural language processing and computer vision applications in land registry, cadastre and land management; to enable effective use of AI tools in professional processes; and to evaluate the ethical and legal dimensions of these technologies.

Course Content

Concept and historical development of artificial intelligence, fundamentals of machine learning and deep learning, natural language processing and large language models, image processing and computer vision, AI applications in real estate valuation, automatic parcel detection from satellite and UAV imagery, digitisation of cadastral documents and OCR, AI-assisted automation in land registry procedures, GIS and spatial AI analysis, use of AI in risk detection, TKGM digital transformation projects, AI ethics, data security and professional responsibility.

Name of Lecturer(s)
Learning Outcomes
1.Explains the fundamental concepts, methods and historical development of artificial intelligence.
2.Defines machine learning and deep learning methods and associates them with professional application examples.
3.Effectively uses large language models and natural language processing tools in professional processes.
4.It evaluates the digitization of cadastral documents and computer vision applications.
5.güvenliği ve KVKK kapsamında mesleki sorumluluklarını tanımlar.Defines professional responsibilities within the scope of AI ethics, algorithmic bias, data security and KVKK.
Recommended or Required Reading
1.Yıldız, K. & Çelik, M. (2023). Yapay Zeka: Temel Kavramlar ve Uygulamalar. Seçkin Yayıncılık.
2.TKGM. e-Tapu ve TAKBİS Kullanım Kılavuzları ve Dijital Dönüşüm Raporları. www.tkgm.gov.tr
3.UN-GGIM. (2023). Geospatial Information and Artificial Intelligence: Opportunities and Challenges for Land Administration. United Nations.
4.Class notes of instructor/lecturer
Weekly Detailed Course Contents
Week 1 - Theoretical
Concept and historical development of AI; key methods: machine learning, deep learning, NLP
Week 2 - Theoretical & Practice
Fundamentals of machine learning: supervised, unsupervised and reinforcement learning; professional examples
Week 3 - Theoretical & Practice
Deep learning and neural networks: basic applications in image and data analysis
Week 4 - Theoretical & Practice
Natural language processing and large language models: professional use of ChatGPT, Copilot and similar tools
Week 5 - Theoretical & Practice
AI in real estate valuation: automated valuation models (AVM) and machine learning methods
Week 6 - Theoretical & Practice
Automatic parcel boundary detection and land cover classification from satellite and UAV imagery
Week 7 - Theoretical & Practice
Digitisation of cadastral documents: OCR, computer vision and conversion of historical land records
Week 8 - Theoretical & Practice
AI-assisted automation in land registry: document classification, fraud detection and error analysis (Midterm Exam)
Week 9 - Theoretical & Practice
GIS and spatial AI: location-based analysis and smart city integration
Week 10 - Theoretical & Practice
Use of AI in detection of risky structures and areas: image analysis and predictive models
Week 11 - Theoretical & Practice
TKGM digital transformation projects: e-Tapu, TAKBİS and AI integration
Week 12 - Theoretical
şeffaflık, hesap verebilirlik ve mesleki sorumlulukAI ethics: algorithmic bias, transparency, accountability and professional responsibility
Week 13 - Theoretical & Practice
Student presentations
Week 14 - Theoretical & Practice
Student presentations
Assessment Methods and Criteria
Type of AssessmentCountPercent
Practice1%10
Presentation1%10
Midterm Examination1%20
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140342
Presentation 2036
Midterm Examination114115
Final Examination114115
TOTAL WORKLOAD (hours)78
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
OÇ-1
4
2
4
4
OÇ-2
4
2
4
4
OÇ-3
1
4
4
4
3
OÇ-4
3
2
3
3
2
4
2
4
3
OÇ-5
3
5
2
4
3
Adnan Menderes University - Information Package / Course Catalogue
2026