Culinary Arts | |||||
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) Define / explain general concepts in culinary profession. | |||||||||||||||
2) Define and explain the internal and external environment relations in which food and beverage businesses are affected. | |||||||||||||||
3) Have knowledge about regulations, professional standards and practices in the field of culinary. | |||||||||||||||
4) Have knowledge and methods on various subjects such as menu planning, cooking methods, world cuisines, regional cuisines and use these knowledge and methods for professional development. | |||||||||||||||
5) Dominates the terminology of food and beverage. | |||||||||||||||
6) Organize all kinds of organizations in the field of culinary. | |||||||||||||||
7) Analyzes and applies the facts about eating and drinking by using the basic concepts and theories of the culinary profession. | |||||||||||||||
8) Takes responsibility as an individual or a team member in the execution of unforeseen and complex activities encountered in the field related applications. | |||||||||||||||
9) Takes risk and responsibility for the realization of information, ideas, applications or technologies that bring innovation to the field. | |||||||||||||||
10) Evaluates the advanced knowledge and skills acquired in the field with a critical approach. | |||||||||||||||
11) Follow current developments in the field and profession. | |||||||||||||||
12) Shares ideas and solutions to problems related to the field by supporting them with qualitative and quantitative data with experts and non-experts. | |||||||||||||||
13) Uses computer software and information technologies at the basic level of at least European computer use license required by the field. | |||||||||||||||
14) Follow the developments in his / her field and communicate with his / her colleagues by using a foreign language (English) at least at the European Language Portfolio B1 General Level. | |||||||||||||||
15) Comply with the social, scientific, cultural and ethical values in the stages of collecting, interpreting, applying and announcing the data related to the field. |
No Effect | 1 Lowest | 2 Average | 3 Highest |
Program Outcomes | Level of Contribution | |
1) | Define / explain general concepts in culinary profession. | |
2) | Define and explain the internal and external environment relations in which food and beverage businesses are affected. | |
3) | Have knowledge about regulations, professional standards and practices in the field of culinary. | |
4) | Have knowledge and methods on various subjects such as menu planning, cooking methods, world cuisines, regional cuisines and use these knowledge and methods for professional development. | |
5) | Dominates the terminology of food and beverage. | |
6) | Organize all kinds of organizations in the field of culinary. | |
7) | Analyzes and applies the facts about eating and drinking by using the basic concepts and theories of the culinary profession. | |
8) | Takes responsibility as an individual or a team member in the execution of unforeseen and complex activities encountered in the field related applications. | |
9) | Takes risk and responsibility for the realization of information, ideas, applications or technologies that bring innovation to the field. | |
10) | Evaluates the advanced knowledge and skills acquired in the field with a critical approach. | |
11) | Follow current developments in the field and profession. | |
12) | Shares ideas and solutions to problems related to the field by supporting them with qualitative and quantitative data with experts and non-experts. | |
13) | Uses computer software and information technologies at the basic level of at least European computer use license required by the field. | |
14) | Follow the developments in his / her field and communicate with his / her colleagues by using a foreign language (English) at least at the European Language Portfolio B1 General Level. | |
15) | Comply with the social, scientific, cultural and ethical values in the stages of collecting, interpreting, applying and announcing the data related to the field. |
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 |