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 | ELECTRICAL AND ELECTRONICS ENGINEERING (MASTER) | ||||
| 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 solve the problems in the field of engineering with data mining approaches. | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 | know data mining. |
PO-2 The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose. PO-8 Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal. |
Examination |
| LO-2 | can define data warehouse and its properties. |
PO-2 The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose. PO-8 Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal. |
Examination |
| LO-3 | can analyze time series. |
PO-2 The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose. PO-8 Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal. |
Examination |
| LO-4 | can create decision trees. |
PO-2 The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose. PO-8 Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal. |
Examination |
| LO-5 | can use classification, clustering and association methods. |
PO-2 The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose. PO-8 Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal. |
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 analysis, ID3 and C4.5 decision tree algorithms, support vector machines, Naive Bayes and k-nearest neighbor classification algorithms, k-means and agglomerative hierarchical clustering algorithms, 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 their 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: MA, WMA, ARMA and ARIMA models | Lecture, question and answer, problem solving |
| 5 | Decision trees: ID3 | Lecture, question and answer, problem solving |
| 6 | Decision trees: C4.5 | Lecture, question and answer, problem solving |
| 7 | Classification: k-nearest neighbor algorithm | Lecture, question and answer, problem solving |
| 8 | mid-term exam | |
| 9 | Classification: k-nearest neighbor algorithm (cont.) | Lecture, question and answer, problem solving |
| 10 | Classification: Naive Bayes algorithm | Lecture, question and answer, problem solving |
| 11 | Classification: Support vector machines | Lecture, question and answer, problem solving |
| 12 | Clustering: Agglomerative hierarchical clustering algorithm | Lecture, question and answer, problem solving |
| 13 | Clustering: k-means algorithm | Lecture, question and answer, problem solving |
| 14 | Clustering: k-means algorithm (cont.) | Lecture, question and answer, problem solving |
| 15 | Association analysis: 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 | ||