Nevşehir Hacı Bektaş Veli University Course Catalogue
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| Year/Semester of Study | 1 / Fall Semester | ||||
| Level of Course | 2nd Cycle Degree Programme | ||||
| Type of Course | Optional | ||||
| Department | TOURISM TECHNOLOGIES AND INNOVATION (MASTER'S WITH THESIS) | ||||
| Pre-requisities and Co-requisites | None | ||||
| Mode of Delivery | Face to Face | ||||
| Teaching Period | 14 Weeks | ||||
| Name of Lecturer | ŞEYHMUS AYDOĞDU (saydogdu@nevsehir.edu.tr) | ||||
| Name of Lecturer(s) | |||||
| Language of Instruction | Turkish | ||||
| Work Placement(s) | None | ||||
| Objectives of the Course | |||||
| The aim of this course is to enable students to use machine learning methods in related problems in the field of tourism by providing students with the basic knowledge and skills related to machine learning, which is the application area of artificial intelligence. | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 | Can explain the basic concepts of artificial intelligence and machine learning. |
PO-2 It can use innovative research methods to collect big data, evaluate it and use it in decision processes. PO-5 Can apply artificial intelligence approaches to tourism problems. PO-6 Distinguish the current technologies applied in the field of tourism and choose the most suitable one. PO-7 Knows the concepts of innovation and technology in the field of tourism. |
Examination |
| LO-2 | Knows the approaches and methods used in machine learning. |
PO-2 It can use innovative research methods to collect big data, evaluate it and use it in decision processes. PO-5 Can apply artificial intelligence approaches to tourism problems. PO-6 Distinguish the current technologies applied in the field of tourism and choose the most suitable one. PO-7 Knows the concepts of innovation and technology in the field of tourism. |
Examination |
| LO-3 | Can apply machine learning methods through a software. |
PO-2 It can use innovative research methods to collect big data, evaluate it and use it in decision processes. PO-5 Can apply artificial intelligence approaches to tourism problems. PO-6 Distinguish the current technologies applied in the field of tourism and choose the most suitable one. PO-7 Knows the concepts of innovation and technology in the field of tourism. PO-8 Gain knowledge of price determination in the tourism sector, estimation of seasonal demands, and personalized recommendations. |
Examination Term Paper |
| LO-4 | Can interpret patterns in big data by revealing them with machine learning techniques. |
PO-2 It can use innovative research methods to collect big data, evaluate it and use it in decision processes. PO-5 Can apply artificial intelligence approaches to tourism problems. PO-6 Distinguish the current technologies applied in the field of tourism and choose the most suitable one. PO-7 Knows the concepts of innovation and technology in the field of tourism. PO-10 Knows how to apply different management tools in tourism business with special emphasis on information and communication technologies and indicative measurements. PO-15 Gain problem solving skills by using research methods used in different disciplines. |
Examination Term Paper |
| LO-5 | Can develop machine learning application for solving a problem in the tourism sector. |
PO-2 It can use innovative research methods to collect big data, evaluate it and use it in decision processes. PO-5 Can apply artificial intelligence approaches to tourism problems. PO-6 Distinguish the current technologies applied in the field of tourism and choose the most suitable one. PO-11 Gains entrepreneurial spirit and takes initiative to initiate innovation and technology related projects in the field of tourism. |
Examination Term Paper |
| PO: Programme Outcomes MME:Method of measurement & Evaluation |
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| Course Contents | ||
| Artificial intelligence, machine learning, supervised learning, unsupervised learning, semi-supervised learning, reinforced learning, data sets in tourism, sample problem situations, applications of machine learning techniques in tourism | ||
| Weekly Course Content | ||
| Week | Subject | Learning Activities and Teaching Methods |
| 1 | Fundamentals of artificial intelligence and machine learning | Lecture, Question and Answer, Discussion |
| 2 | Approaches and methods used in machine learning | Lecture, Question and Answer, Discussion |
| 3 | Software to be used in machine learning and python programming language | Lecture, Question and Answer, Discussion, Individual Study Method |
| 4 | Python programming applications | Lecture, Question and Answer, Discussion, Individual Study Method |
| 5 | Supervised learning applications | Lecture, Question and Answer, Discussion, Individual Study Method |
| 6 | Supervised learning applications | Lecture, Question and Answer, Discussion, Individual Study Method |
| 7 | Supervised learning applications | Lecture, Question and Answer, Discussion, Individual Study Method |
| 8 | mid-term exam | |
| 9 | Unsupervised learning applications | Lecture, Question and Answer, Discussion, Individual Study Method |
| 10 | Unsupervised learning applications | Lecture, Question and Answer, Discussion, Individual Study Method |
| 11 | Unsupervised learning applications | Lecture, Question and Answer, Discussion, Individual Study Method |
| 12 | Semi-supervised learning applications | Lecture, Question and Answer, Discussion, Individual Study Method |
| 13 | Semi-supervised learning applications | Lecture, Question and Answer, Discussion, Individual Study Method |
| 14 | Machine learning applications in solving problems in the tourism sector | Lecture, Question and Answer, Discussion, Individual Study Method |
| 15 | Machine learning applications in solving problems in the tourism sector | Lecture, Question and Answer, Discussion, Individual Study Method |
| 16 | final exam | |
| Recommend Course Book / Supplementary Book/Reading | ||
| 1 | Alpaydın, E. (2013). Yapay öğrenme. Boğaziçi Üniversitesi Yayınevi. | |
| 2 | Müller, A. C., & Guido, S. (2016). Introduction to machine learning with Python: a guide for data scientists. " O'Reilly Media, Inc.". | |
| 3 | Aydoğdu, Ş. (2020). Algoritma ve Programlama. Pegem Akademi: Ankara. | |
| 4 | Han, J., Kamber, M., & Pei, J. (2012). Data mining concepts and techniques third edition. University of Illinois at Urbana-Champaign Micheline Kamber Jian Pei Simon Fraser University. | |
| Required Course instruments and materials | ||
| textbook, laptop | ||
| Assessment Methods | |||
| Type of Assessment | Week | Hours | Weight(%) |
| mid-term exam | 8 | 1 | 40 |
| Other assessment methods | |||
| 1.Oral Examination | |||
| 2.Quiz | |||
| 3.Laboratory exam | |||
| 4.Presentation | |||
| 5.Report | |||
| 6.Workshop | |||
| 7.Performance Project | |||
| 8.Term Paper | 16 | 1 | 30 |
| 9.Project | |||
| final exam | 16 | 1 | 30 |
| Student Work Load | |||
| Type of Work | Weekly Hours | Number of Weeks | Work Load |
| Weekly Course Hours (Theoretical+Practice) | 3 | 14 | 42 |
| Outside Class | |||
| a) Reading | 4 | 10 | 40 |
| b) Search in internet/Library | 0 | ||
| c) Performance Project | 0 | ||
| d) Prepare a workshop/Presentation/Report | 0 | ||
| e) Term paper/Project | 4 | 11 | 44 |
| Oral Examination | 0 | ||
| Quiz | 0 | ||
| Laboratory exam | 0 | ||
| Own study for mid-term exam | 4 | 7 | 28 |
| mid-term exam | 1 | 1 | 1 |
| Own study for final exam | 4 | 7 | 28 |
| final exam | 1 | 1 | 1 |
| 0 | |||
| 0 | |||
| Total work load; | 184 | ||