BBP326 Data Mining Applications in Horticulture

3 ECTS - 2-0 Duration (T+A)- 6. Semester- 2 National Credit

Information

Code BBP326
Name Data Mining Applications in Horticulture
Term 2022-2023 Academic Year
Semester 6. Semester
Duration (T+A) 2-0 (T-A) (17 Week)
ECTS 3 ECTS
National Credit 2 National Credit
Teaching Language Türkçe
Level Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Prof. Dr. ZEYNEL CEBECİ
Course Instructor Prof. Dr. ZEYNEL CEBECİ (A Group) (Ins. in Charge)


Course Goal / Objective

Learning the methods and techniques for data mining and artificial neural networks in horticulture, applications and analysis with the relevant methods

Course Content

Data Mining applications on research and production data in horticulture

Course Precondition

To have taken the following courses before: Introductory Statistics Introduction to Information Technologies

Resources

Cebeci, Z. (2020). Data Preprocessing with R in Data Science. Nobel Akademik Yayıncılık. ISBN 9786254060755

Notes

Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Machine Learning: Fundamental Concepts and Algorithms, 2nd Edition, Cambridge University Press, March 2020. ISBN: 978-1108473989. https://dataminingbook.info/book_html/


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Learn the terms in data science and data mining, and organize, edit and analyze the horticultural data and data sources.
LO02 Learn how to use R Statistical computing and graphics environment.
LO03 Learn the data preprocessing techniques and the methods for data visualization.
LO04 Learn the unsupervised and supervised learning methods for classification and clustering.
LO05 Learn and apply the artificial neural networks.and deep learning


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Adequate knowledge on subjects specific to the discipline of Mathematics, Science and Agricultural Engineering (Horticulture), ability to use theoretical and applied knowledge in these fields in complex engineering problems 5
PLO02 Bilgi - Kuramsal, Olgusal The ability to identify and solve problems related to the cultivation, breeding and product preservation of fruit, vegetables, vineyards and ornamental plants in horticulture, the ability to choose and apply appropriate analysis and modeling methods for this purpose. 1
PLO03 Beceriler - Bilişsel, Uygulamalı The ability to design in a way that meets the necessary conditions for the cultivation of fruit, vegetables, vineyards and ornamental plants in the open and greenhouse in horticulture and the ability to apply modern design methods for this purpose.
PLO04 Beceriler - Bilişsel, Uygulamalı Ability to select and use modern tools necessary for the analysis and solution of complex problems encountered in horticulture practices, ability to use information technologies effectively 4
PLO05 Beceriler - Bilişsel, Uygulamalı Ability to design and conduct experiments, collect data, analyze and interpret results for the study of complex problems or discipline-specific research issues in the field of Horticulture 3
PLO06 Beceriler - Bilişsel, Uygulamalı Breeding of Horticultural Plants, developing new varieties, making selection, protecting genetic resources, producing propagation materials (seeds, seedlings, saplings) of developed varieties, ability to work in individual and multi-disciplinary teams
PLO07 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Ability to write effective reports in the field of Horticulture, to understand written reports, to prepare design and production reports, to make effective presentations, to take and give clear and understandable instructions 1
PLO08 Yetkinlikler - Öğrenme Yetkinliği Awareness of the necessity of lifelong learning, the ability to access information in the field of Horticulture, to follow the developments in science and technology and to constantly renew oneself
PLO09 Yetkinlikler - Öğrenme Yetkinliği Behaving in accordance with ethical principles, professional and ethical responsibility in the field of Horticulture, and knowledge of standards used in engineering practices
PLO10 Yetkinlikler - İletişim ve Sosyal Yetkinlik Information about applications in business life, such as project management, risk management and change management in the field of Horticulture, awareness of entrepreneurship, innovation, information about sustainable development
PLO11 Yetkinlikler - Alana Özgü Yetkinlik Knowledge of the effects of horticultural practices on health, environment and safety in universal and social dimensions and the problems of the age reflected in the field of Horticulture, awareness of the legal consequences of horticultural solutions


Week Plan

Week Topic Preparation Methods
1 Introduction to data science and data mining Studying lecture notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
2 Agricultural data and data types Studying lecture notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
3 Statistical computing and visualization with R Studying lecture notes, Install and run with R software Öğretim Yöntemleri:
Anlatım, Soru-Cevap
4 Data preprocessing Studying lecture notes, Build the data sets in R Öğretim Yöntemleri:
Anlatım, Soru-Cevap
5 Data visualization Studying lecture notes, Drawing histograms, scatterplot, barplot, pie graphs in R Öğretim Yöntemleri:
Anlatım, Soru-Cevap
6 Statistical data mining methods 1 Studying lecture notes, Reading the tutorials on the Web Öğretim Yöntemleri:
Anlatım, Soru-Cevap
7 Statistical data mining methods 2 Studying lecture notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
8 Mid-Term Exam Studying lecture notes Ölçme Yöntemleri:
Yazılı Sınav
9 Statistical data mining methods 3 Studying lecture notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
10 Undsupervised learning techniques Studying lecture notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
11 Supervised learning techniques Studying lecture notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
12 Artificial Neural Networks 1 Studying lecture notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
13 Artificial Neural Networks 2 Studying lecture notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
14 Deep Learning applications 1 Studying lecture notes Öğretim Yöntemleri:
Alıştırma ve Uygulama
15 Deep Learning applications 2 Studying lecture notes Öğretim Yöntemleri:
Anlatım, Soru-Cevap
16 Term Exams Studying lecture notes Ölçme Yöntemleri:
Yazılı Sınav
17 Term Exams Studying 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

Update Time: 17.11.2022 10:40