Nevşehir Hacı Bektaş Veli University Course Catalogue
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| Year/Semester of Study | 1 / Fall Semester | ||||
| Level of Course | 3rd Cycle Degree Programme | ||||
| Type of Course | Optional | ||||
| Department | ELEKTRIK-ELEKTRONIK MüHENDISLIğI ANABILIM DALı DOKTORA (ÖNERILEN PROGRAM) | ||||
| Pre-requisities and Co-requisites | None | ||||
| Mode of Delivery | Face to Face | ||||
| Teaching Period | 14 Weeks | ||||
| Name of Lecturer | MEHMET YEŞİLBUDAK (myesilbudak@nevsehir.edu.tr) | ||||
| Name of Lecturer(s) | |||||
| Language of Instruction | Turkish | ||||
| Work Placement(s) | None | ||||
| Objectives of the Course | |||||
| To teach data mining and to gain the ability to apply data mining methods to the problems in the field of engineering. | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 | know data mining. |
PO-1 Conducting scientific research on subjects specific to the Electrical and Electronics Engineering discipline, interpreting this information and gaining application skills. PO-2 Ability to develop, select and use modern techniques and tools required for the analysis and solution of complex problems encountered in engineering applications; has the ability to use information technologies effectively. PO-7 Awareness of the necessity of lifelong learning; ability to access information, follow developments in science and technology and constantly renew oneself. |
Examination |
| LO-2 | can define data warehouse and its properties. |
PO-1 Conducting scientific research on subjects specific to the Electrical and Electronics Engineering discipline, interpreting this information and gaining application skills. PO-2 Ability to develop, select and use modern techniques and tools required for the analysis and solution of complex problems encountered in engineering applications; has the ability to use information technologies effectively. PO-7 Awareness of the necessity of lifelong learning; ability to access information, follow developments in science and technology and constantly renew oneself. |
Examination |
| LO-3 | can apply time series. |
PO-1 Conducting scientific research on subjects specific to the Electrical and Electronics Engineering discipline, interpreting this information and gaining application skills. PO-2 Ability to develop, select and use modern techniques and tools required for the analysis and solution of complex problems encountered in engineering applications; has the ability to use information technologies effectively. PO-7 Awareness of the necessity of lifelong learning; ability to access information, follow developments in science and technology and constantly renew oneself. |
Examination |
| LO-4 | can apply decision trees. |
PO-1 Conducting scientific research on subjects specific to the Electrical and Electronics Engineering discipline, interpreting this information and gaining application skills. PO-2 Ability to develop, select and use modern techniques and tools required for the analysis and solution of complex problems encountered in engineering applications; has the ability to use information technologies effectively. PO-7 Awareness of the necessity of lifelong learning; ability to access information, follow developments in science and technology and constantly renew oneself. |
Examination |
| LO-5 | can apply classification, clustering and association methods. |
PO-1 Conducting scientific research on subjects specific to the Electrical and Electronics Engineering discipline, interpreting this information and gaining application skills. PO-2 Ability to develop, select and use modern techniques and tools required for the analysis and solution of complex problems encountered in engineering applications; has the ability to use information technologies effectively. PO-7 Awareness of the necessity of lifelong learning; ability to access information, follow developments in science and technology and constantly renew oneself. |
Examination |
| PO: Programme Outcomes MME:Method of measurement & Evaluation |
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| Course Contents | ||
| Introduction to data mining, database, data models, data warehouse and its properties, data cleaning, data integration, data reduction and data transformation, time series application, applications of ID3 and C4.5 decision tree algorithms, applications of support vector machines, Naive Bayes and k-nearest neighbor classification algorithms, applications of k-means and agglomerative hierarchical clustering algorithms, application of Apriori association algorithm. | ||
| Weekly Course Content | ||
| Week | Subject | Learning Activities and Teaching Methods |
| 1 | Introduction to data mining | Lecture, question and answer, problem solving |
| 2 | Database, data models, data warehouse and its properties | Lecture, question and answer, problem solving |
| 3 | Data cleaning, data integration, data reduction and data transformation | Lecture, question and answer, problem solving |
| 4 | Time series analysis: Applications of MA, WMA, ARMA and ARIMA models | Lecture, question and answer, problem solving |
| 5 | Decision trees: Application of ID3 algorithm | Lecture, question and answer, problem solving |
| 6 | Decision trees: Application of C4.5 algorithm | Lecture, question and answer, problem solving |
| 7 | Classification: Application of k-nearest neighbor algorithm | Lecture, question and answer, problem solving |
| 8 | mid-term exam | |
| 9 | Classification: Application of k-nearest neighbor algorithm (cont.) | Lecture, question and answer, problem solving |
| 10 | Classification: Application of Naive Bayes algorithm | Lecture, question and answer, problem solving |
| 11 | Classification: Application of support vector machines | Lecture, question and answer, problem solving |
| 12 | Clustering: Application of agglomerative hierarchical clustering algorithm | Lecture, question and answer, problem solving |
| 13 | Clustering: Application of k-means algorithm | Lecture, question and answer, problem solving |
| 14 | Clustering: Application of k-means algorithm (cont.) | Lecture, question and answer, problem solving |
| 15 | Association analysis: Application of Apriori algorithm | Lecture, question and answer, problem solving |
| 16 | final exam | |
| Recommend Course Book / Supplementary Book/Reading | ||
| 1 | Veri Madenciliği Yöntemleri, Y. Özkan, Papatya Yayıncılık, 2008. | |
| 2 | Data Mining: Concepts and Techniques, J. Han, M. Kamber, Morgan Kaufmann Pub., 2006. | |
| Required Course instruments and materials | ||
| Course book, computer, projector. | ||
| 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 | |||
| 9.Project | |||
| final exam | 16 | 1 | 60 |
| 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 | 3 | 14 | 42 |
| b) Search in internet/Library | 3 | 14 | 42 |
| c) Performance Project | 0 | ||
| d) Prepare a workshop/Presentation/Report | 0 | ||
| e) Term paper/Project | 0 | ||
| Oral Examination | 0 | ||
| Quiz | 0 | ||
| Laboratory exam | 0 | ||
| Own study for mid-term exam | 2 | 13 | 26 |
| mid-term exam | 1 | 1 | 1 |
| Own study for final exam | 2 | 13 | 26 |
| final exam | 1 | 1 | 1 |
| 0 | |||
| 0 | |||
| Total work load; | 180 | ||