TÜRKÇE
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Information
| Unit | FACULTY OF ENGINEERING |
| MINING ENGINEERING PR. | |
| Code | MDS440 |
| Name | Artificial Intelligence Applications in Mining |
| Term | 2026-2027 Academic Year |
| Semester | 8. Semester |
| Duration (T+A) | 2-0 (T-A) (17 Week) |
| ECTS | 3 ECTS |
| National Credit | 2 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Label | E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Doç. Dr. Ali Can ÖZDEMİR |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The aim of this course is to introduce the application of artificial intelligence and machine learning techniques in the mining industry, and to provide an understanding of their use in areas such as data analysis, orebody modeling, production optimization, predictive maintenance, and decision support systems.
Course Content
This course covers an introduction to artificial intelligence and machine learning, data preprocessing techniques, supervised and unsupervised learning methods, and fundamentals of deep learning.
Course Precondition
None
Resources
Applications of Artificial Intelligence in Mining and Geotechnical Engineering (Hoang Nguyen, Xuan Nam Bui, Erkan Topal, Jian Zhou, Yosoon Choi, Wengang Zhang), 2023
Notes
Lecture Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explain the fundamental concepts of artificial intelligence and machine learning. |
| LO02 | Describe the structure of mining data and appropriate analysis methods. |
| LO03 | Apply data preprocessing, cleaning, and feature engineering techniques. |
| LO04 | Implement supervised and unsupervised learning algorithms to mining problems. |
| LO05 | Develop data-driven approaches for production planning and optimization. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | PÇ1. (a) Adequate knowledge of mathematics, basic sciences, and discipline-specific topics in Mining Engineering; PÇ1. (b) the ability to use theoretical and applied knowledge in these areas for solving complex engineering problems. | |
| PLO02 | Beceriler - Bilişsel, Uygulamalı | PÇ2. (a) Ability to identify, formulate, and solve complex problems in Mining Engineering; PÇ2. (b) ability to select and apply appropriate analysis and modeling methods for this purpose. | 4 |
| PLO03 | Beceriler - Bilişsel, Uygulamalı | PÇ3. (a) Ability to design a complex system, process, device, or product to meet specified requirements under realistic constraints and conditions; PÇ3. (b) ability to apply modern design methods for this purpose. | |
| PLO04 | Beceriler - Bilişsel, Uygulamalı | PÇ4. (a) Ability to select and use modern technical tools necessary for the analysis and solution of complex problems encountered in Mining Engineering applications; PÇ4. (b) ability to effectively use information technologies. | 4 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | PÇ5. Ability to design experiments, conduct experiments, collect data, analyze and interpret results for the investigation of problems specific to Mining Engineering. | |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | PÇ6. (a) Ability to work effectively in disciplinary (Mining Engineering) and multidisciplinary teams; PÇ6. (b) ability to work individually. | |
| PLO07 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | PÇ7. (a) Ability to communicate effectively in Turkish, both orally and in writing; PÇ7. (b) knowledge of at least one foreign language; ability to write effective reports and understand written reports, prepare design and production reports, deliver effective presentations, and give and receive clear and understandable instructions. | |
| PLO08 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | PÇ8. Awareness of the necessity of lifelong learning; ability to access information, follow developments in science and technology, and continuously improve oneself. | |
| PLO09 | Yetkinlikler - Öğrenme Yetkinliği | PÇ9. Ability to act in accordance with the ethical principles of Mining Engineering; knowledge of professional and ethical responsibilities and of the standards used in engineering practice. | |
| PLO10 | Yetkinlikler - Öğrenme Yetkinliği | PÇ10. Knowledge of business-life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development. | |
| PLO11 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | PÇ11. Knowledge of the impacts of Mining Engineering practices on health, environment, and safety at universal and societal levels, as well as contemporary issues in engineering; awareness of the legal consequences of Mining Engineering solutions. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to digital transformation and artificial intelligence in mining | Literature review | Öğretim Yöntemleri: Anlatım |
| 2 | Fundamentals of artificial intelligence and machine learning | Literature review | Öğretim Yöntemleri: Anlatım |
| 3 | Mining data structures and data preprocessing techniques | Literature review | Öğretim Yöntemleri: Anlatım |
| 4 | Supervised learning methods (regression, classification) | Literature review | Öğretim Yöntemleri: Anlatım |
| 5 | Unsupervised learning methods (clustering, dimensionality reduction) | Literature review | Öğretim Yöntemleri: Anlatım |
| 6 | Introduction to deep learning and basic architectures | Literature review | Öğretim Yöntemleri: Anlatım |
| 7 | Drill data analysis and orebody modeling applications | Literature review | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Reserve estimation and resource modeling techniques | Literature review | Öğretim Yöntemleri: Anlatım |
| 10 | Production planning and optimization (AI-based approaches) | Literature review | Öğretim Yöntemleri: Anlatım |
| 11 | Predictive maintenance and equipment performance analysis | Literature review | Öğretim Yöntemleri: Anlatım |
| 12 | Image processing and rock/ore classification | Literature review | Öğretim Yöntemleri: Anlatım |
| 13 | Autonomous mining systems and sensor technologies | Literature review | Öğretim Yöntemleri: Anlatım |
| 14 | Big data, IoT, and GIS integration | Literature review | Öğretim Yöntemleri: Anlatım |
| 15 | Overall evaluation | Literature review | Öğretim Yöntemleri: Soru-Cevap |
| 16 | Term Exams | Lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 2 | 28 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 2 | 28 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 6 | 6 |
| Final Exam | 1 | 16 | 16 |
| Total Workload (Hour) | 78 | ||
| Total Workload / 25 (h) | 3,12 | ||
| ECTS | 3 ECTS | ||