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Information
| Code | ZO019 |
| Name | Agricultural Data Mining with R |
| Term | 2022-2023 Academic Year |
| Term | Spring |
| Duration (T+A) | 4-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 4 National Credit |
| Teaching Language | Türkçe |
| Level | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. ZEYNEL CEBECİ |
| Course Instructor |
1 |
Course Goal / Objective
This course aims to teach the topics on data mining methods and algorithms with their applications in agriculture.
Course Content
This course includes the topics on data mining methods and algorithms with their applications in agriculture.
Course Precondition
No prerequisites
Resources
Cebeci, Z., Tekeli, E., Tahtalı, Y. (2022). Machine Learning and Data Mining with R in Agriculture, Food and Life Sciences. Nobel Akademik Yayıncılık, Ankara.
Notes
François Chollet, F., Allaire, J.J. (2018). Deep Learning with R. ISBN 9781617295546 URL https://www.manning.com/books/deep-learning-with-r
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Learns the concepts and terminology related with data mining and machine learning |
| LO02 | Uses the data mining and machine learning software |
| LO03 | Learns the algoritms for data mining and machine learning. |
| LO04 | Compares the performances of the data mining and machine learning algorithms. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | After undergraduate education, increases knowledge in one of the fields of animal breeding and breeding, feeds and animal nutrition, biometrics and genetics. | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Understands the interaction between different disciplines | 2 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Gains the ability to develop strategic approaches and produce regional, national or international solutions for the field of animal science | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Gains the ability to develop knowledge with scientific methods by using the data in animal science, and to use this knowledge with the awareness of scientific, social and ethical responsibility. | 2 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Gains the ability to use and develop information technologies with computer software and hardware knowledge required by the field of animal science. | 5 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Gains the ability to convey their own studies or current developments in the field of animal science to groups in the field or other fields of science, verbally and visually. | |
| PLO07 | Bilgi - Kuramsal, Olgusal | Gains the ability to evaluate the quality processes of animal products | |
| PLO08 | Bilgi - Kuramsal, Olgusal | Gains the ability to keep animal production dynamic in accordance with changing economic and social conditions. | |
| PLO09 | Bilgi - Kuramsal, Olgusal | Gains the ability to follow national and international current issues, to follow developments in lifelong learning, science and technology, to constantly renew themselves and to transfer innovations to animal production. | |
| PLO10 | Bilgi - Kuramsal, Olgusal | Hayvansal ürünler ile insan sağlığı ve toplum refahı arasındaki ilişkiyi özümser |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to data mining | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | |
| 2 | Data mining software and tools | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | |
| 3 | R and R packages for data mining | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | |
| 4 | Data preparation for data mining | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | |
| 5 | Summarization and visualization | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | |
| 6 | Discretization | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | |
| 7 | Association analysis | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | |
| 8 | Mid-Term Exam | Preparation for the exam | |
| 9 | Cluster analysis | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | |
| 10 | Outlier detection | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | |
| 11 | Fundementals of classification | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | |
| 12 | Classification and decision trees | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | |
| 13 | Classification with C4.5 | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | |
| 14 | Random forests | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | |
| 15 | Case study | On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. | |
| 16 | Term Exams | Preparation for the exam | |
| 17 | Term Exams | Preparation for the exam |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 4 | 56 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 4 | 56 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 28 | 28 |
| Total Workload (Hour) | 152 | ||
| Total Workload / 25 (h) | 6,08 | ||
| ECTS | 6 ECTS | ||