| Course Unit Code | Course Unit Title | Type of Course Unit | Year of Study | Semester | Number of ECTS Credits | | OTD524 | Artificial Intelligence Applications in Agriculture | Elective | 1 | 2 | 6 |
|
| Level of Course Unit |
| Third Cycle |
| Objectives of the Course |
| 1-Introducing and popularizing data mining
2-To be able to develop artificial intelligence models |
| Name of Lecturer(s) |
| Doç.Dr. Didem Güleryüz |
| Learning Outcomes |
| 1 | 1. Students will gain knowledge and skills to learn and apply the basic concepts of Data Mining. | | 2 | 2. Students will learn data preprocessing-(Data cleaning, merging) methods. | | 3 | 3. Students will learn data reduction methods | | 4 | 4. Students will learn classification and clustering methods with and without a trainer. | | 5 | 5. Students will learn the agricultural applications of artificial | | 6 | 6. Students will be able to develop basic models based on artificial intelligence. |
|
| Mode of Delivery |
| Normal Education |
| Prerequisites and co-requisities |
| None |
| Recommended Optional Programme Components |
|
| Course Contents |
| This course covers the fundamentals of data mining and machine learning methods to develop Artificial Intelligence applications. The course consists of three parts. The first part is about the basics of statistics and machine learning approach for data mining. In the second part, the basic artificial intelligence model development framework will be discussed. In the last part of the course, projects using artificial intelligence-based models will be examined and a framework will be created in order to develop a new model. |
| Weekly Detailed Course Contents |
|
| 1 | Introduction and General Definitions | | | | 2 | Application Fields of Data Mining | | | | 3 | Introducing computer programs used in Data Mining | | | | 4 | Decision Trees | | | | 5 | Classification Algorithms | | | | 6 | Clustering Algorithms | | | | 7 | Artificial neural networks | | | | 8 | Midterm Exam | | | | 9 | Stages from Data Mining to Artificial Intelligence models | | | | 10 | Association Rules | | | | 11 | Artificial Intelligence Applications in Agriculture - Project Development | | | | 12 | Artificial Intelligence Applications in Agriculture - Project Development | | | | 13 | Artificial Intelligence Applications in Agriculture - Project Development | | | | 14 | Artificial Intelligence Applications in Agriculture - Project Development | | | | 15 | Artificial Intelligence Applications in Agriculture - Project Development | | |
|
| Recommended or Required Reading |
| Gökhan Silahtaroğlu, Kavram ve Algoritmalarıyla Temel Veri Madenciliği, Papatya Yayıncılık (2008)
Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Addison Wesley, (2005).
İş zekası ve veri madenciliği, Şadi Evren Şeker, Cinius Yayınları
Data Mining: Concepts and Techniques, Jiawei Han, Jian Pei, Micheline Kamber,
Intelligent Data Mining and Fusion Systems in Agriculture, Xanthoula Eirini Pantazi, Dimitrios Moshou, |
| Planned Learning Activities and Teaching Methods |
|
| Assessment Methods and Criteria | |
| Midterm Examination | 1 | 100 | | SUM | 100 | |
| Final Examination | 1 | 100 | | SUM | 100 | | Term (or Year) Learning Activities | 30 | | End Of Term (or Year) Learning Activities | 70 | | SUM | 100 |
| | Language of Instruction | | Turkish | | Work Placement(s) | |
|
| Workload Calculation |
|
| Midterm Examination | 1 | 1 | 1 |
| Final Examination | 1 | 2 | 2 |
| Quiz | 2 | 1 | 2 |
| Attending Lectures | 14 | 3 | 42 |
| Project Preparation | 2 | 15 | 30 |
| Project Presentation | 1 | 1 | 1 |
| Criticising Paper | 5 | 3 | 15 |
| Individual Study for Mid term Examination | 4 | 5 | 20 |
| Individual Study for Final Examination | 5 | 4 | 20 |
| Individual Study for Quiz | 2 | 5 | 10 |
| Homework | 5 | 5 | 25 |
|
| Contribution of Learning Outcomes to Programme Outcomes |
| LO1 | 4 | 4 | 3 | 3 | 5 | 5 | 5 | 3 | 4 | 4 | 4 | 4 | 3 | 4 | 4 | 4 | 5 | 4 | 5 | | | 3 | 4 | | | LO2 | 2 | 5 | 4 | 4 | 4 | 4 | 4 | 4 | 5 | 5 | 4 | 4 | 3 | 5 | 5 | 5 | 4 | 5 | 4 | | | 3 | 5 | | | LO3 | 4 | 5 | 3 | 5 | 2 | 3 | 5 | 4 | 4 | 5 | 4 | 5 | 3 | 4 | 4 | 3 | 4 | 5 | 3 | | | 4 | 4 | | | LO4 | 3 | 3 | 5 | 2 | 5 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 3 | 4 | 5 | 4 | 5 | 4 | 4 | | | 4 | 5 | | | LO5 | 4 | 5 | 4 | 3 | 5 | 4 | 5 | 5 | 5 | 5 | 4 | 5 | 4 | 5 | 5 | 5 | 5 | 5 | 5 | | | 4 | 5 | | | LO6 | 4 | 5 | 4 | 4 | 4 | 5 | 3 | 5 | 4 | 4 | 5 | 4 | 5 | 4 | 4 | 4 | 5 | 4 | 4 | | | 5 | 5 | |
|
| * Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High |
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