Nevşehir Hacı Bektaş Veli University Course Catalogue
|
|||||
| 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 and data warehouse. |
PO-1 Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems. PO-2 The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose. PO-4 Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively. PO-5 Ability to design experiments, conduct experiments, collect data, analyze and interpret results for examination of engineering problems. 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 analyze time series and decision trees. |
PO-1 Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems. PO-2 The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose. PO-4 Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively. PO-5 Ability to design experiments, conduct experiments, collect data, analyze and interpret results for examination of engineering problems. 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 use classification methods in data mining. |
PO-1 Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems. PO-2 The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose. PO-4 Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively. PO-5 Ability to design experiments, conduct experiments, collect data, analyze and interpret results for examination of engineering problems. 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 use clustering methods in data mining. |
PO-1 Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems. PO-2 The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose. PO-4 Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively. PO-5 Ability to design experiments, conduct experiments, collect data, analyze and interpret results for examination of engineering problems. 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 association analysis methods in data mining. |
PO-1 Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems. PO-2 The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose. PO-4 Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively. PO-5 Ability to design experiments, conduct experiments, collect data, analyze and interpret results for examination of engineering problems. 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 |
|||
| 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 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: MA, WMA, ARMA and ARIMA models | Lecture, question and answer, problem solving |
| 5 | Decision trees analysis: ID3 algorithm | Lecture, question and answer, problem solving |
| 6 | Decision trees analysis: C4.5 algorithm | 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 | 6 | 14 | 84 |
| 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 | 1 | 5 | 5 |
| mid-term exam | 1 | 1 | 1 |
| Own study for final exam | 1 | 5 | 5 |
| final exam | 1 | 1 | 1 |
| 0 | |||
| 0 | |||
| Total work load; | 180 | ||