ZO019 Agricultural Data Mining with R

6 ECTS - 4-0 Duration (T+A)- . Semester- 4 National Credit

Information

Code ZO019
Name Agricultural Data Mining with R
Term 2022-2023 Academic Year
Semester . Semester
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 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. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
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. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
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. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
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. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
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. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
6 Discretization On the Internet, searching for the learning resources, reading the tutorials, lecture notes and textbooks, and problem solving related with the topic. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
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. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
8 Mid-Term Exam Preparation for the exam Ölçme Yöntemleri:
Ödev, Sözlü Sınav
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. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
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. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
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. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
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. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
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. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
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. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
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. Öğretim Yöntemleri:
Alıştırma ve Uygulama
16 Term Exams Preparation for the exam Ölçme Yöntemleri:
Sözlü Sınav, Ödev
17 Term Exams Preparation for the exam Ölçme Yöntemleri:
Sözlü Sınav, Ödev


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

Update Time: 17.11.2022 01:34