E-Commerce and Marketing | |||||
Associate | TR-NQF-HE: Level 5 | QF-EHEA: Short Cycle | EQF-LLL: Level 5 |
Course Code: | UNI220 | ||||
Course Name: | Machine Learning and Data Science | ||||
Semester: | Spring | ||||
Course Credits: |
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Language of instruction: | Turkish | ||||
Course Condition: | |||||
Does the Course Require Work Experience?: | No | ||||
Type of course: | University Elective | ||||
Course Level: |
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Mode of Delivery: | E-Learning | ||||
Course Coordinator: | Dr. Öğr. Üy. ALPER ÖNER | ||||
Course Lecturer(s): | Ferzat Anka | ||||
Course Assistants: |
Course Objectives: | The aim of the course is to provide students with information on basic techniques and methods in artificial learning and to enable students to have the ability to use artificial learning methods in solving practical problems. At the same time, it is to understand the importance of machine learning in today's application areas. |
Course Content: | Machine learning basic concepts and methods. Problem solving using machine learning; methods using and not using problem information. Data analysis, To examine various algorithms. To explain the importance of artificial intelligence methods in different fields with examples |
The students who have succeeded in this course;
1) • Recognize the problems that can be solved by machine learning methods. 2) • Understanding the importance of artificial intelligence in solving various problems 3) • Can choose the appropriate machine learning method for the given problem. 4) • Can solve the given problem with the appropriate machine learning method. 5) • Knows the ways of representing information, its advantages and disadvantages. |
Week | Subject | Related Preparation |
1) | Machine learning history and philosophy | |
2) | Basic concepts | |
3) | Basic concepts-Intelligent Agents | |
4) | Introduction to machine learning and problem solving and search algorithms | |
5) | Expert systems and machine learning | |
6) | Optimization methods in machine learning | |
7) | Homework-Presentation | |
8) | Homework-Presentation | |
9) | Homework-Presentation | |
10) | Data science and analysis | |
11) | Machine learning | |
12) | Data science and methods | |
13) | Machine learning | |
14) | Search algorithms and their importance (Definite, greedy, heuristic, meta-heuristic) |
Course Notes / Textbooks: | • Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, • Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition • Vasif Nabiyev, Yapay Zeka: İnsan ve Bilgisayar Etkileşimi 4. Baskı • Yalçin Özkan, Veri Madenciliği Yöntemleri, Papatya, 2008 • Cemalettin Kubat, Matlab Yapay Zeka ve Mühendislik uygulamaları, Pusula, 2009 • İlker Arslan, R ile İstatistiksel Programlama, Pusula, 2020 • Zafer Demirkol, Herkes İçin Yapay Zeka, Genç Destek, 2021 • S.Nematzadeh et al. Rationalized Statistics for Biosciences Analysing bioinformatics data using the R, LAP Publishing, 2021 |
References: | • Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, • Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition • Vasif Nabiyev, Yapay Zeka: İnsan ve Bilgisayar Etkileşimi 4. Baskı • Yalçin Özkan, Veri Madenciliği Yöntemleri, Papatya, 2008 • Cemalettin Kubat, Matlab Yapay Zeka ve Mühendislik uygulamaları, Pusula, 2009 • İlker Arslan, R ile İstatistiksel Programlama, Pusula, 2020 • Zafer Demirkol, Herkes İçin Yapay Zeka, Genç Destek, 2021 • S.Nematzadeh et al. Rationalized Statistics for Biosciences Analysing bioinformatics data using the R, LAP Publishing, 2021 |
Course Learning Outcomes | 1 |
2 |
3 |
4 |
5 |
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Program Outcomes | |||||
1) To have the ability to understand and apply basic concepts in the field of e-commerce and marketing | |||||
2) Having the ability to catch up with the speed of the digital age and keep their information up to date | |||||
3) Having detailed technical and professional application skills in subjects such as mobile marketing, digital media marketing, social media usage, digital analysis and measurement. |
No Effect | 1 Lowest | 2 Average | 3 Highest |
Program Outcomes | Level of Contribution | |
1) | To have the ability to understand and apply basic concepts in the field of e-commerce and marketing | 2 |
2) | Having the ability to catch up with the speed of the digital age and keep their information up to date | 1 |
3) | Having detailed technical and professional application skills in subjects such as mobile marketing, digital media marketing, social media usage, digital analysis and measurement. | 2 |
Semester Requirements | Number of Activities | Level of Contribution |
Presentation | 1 | % 40 |
Final | 1 | % 60 |
total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 40 | |
PERCENTAGE OF FINAL WORK | % 60 | |
total | % 100 |
Activities | Number of Activities | Workload |
Course Hours | 16 | 48 |
Study Hours Out of Class | 16 | 53 |
Presentations / Seminar | 5 | 10 |
Final | 1 | 2 |
Total Workload | 113 |