Power Electronics and Clean Energy Systems (English) with thesis | |||||
Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 |
Course Code: | AIE509 | ||||||||
Course Name: | Data Mining and Big Data | ||||||||
Course Semester: | Fall | ||||||||
Course Credits: |
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Language of instruction: | EN | ||||||||
Course Requisites: | |||||||||
Does the Course Require Work Experience?: | No | ||||||||
Type of course: | Department Elective | ||||||||
Course Level: |
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Mode of Delivery: | Face to face | ||||||||
Course Coordinator : | Prof. Dr. PINAR YILDIRIM | ||||||||
Course Lecturer(s): |
Dr.Öğr.Üyesi SİNA ALP |
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Course Assistants: |
Course Objectives: | The purpose of the course is to educate students about the main concepts and methods of data mining. The course contains these topics: classification, clustering, association algorithms and data mining studies in different areas. |
Course Content: | The purpose of the course is to educate students about the main concepts and methods of data mining. The course contains these topics: classification, clustering, association algorithms and data mining studies in different areas. |
The students who have succeeded in this course;
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Week | Subject | Related Preparation |
1) | • What Motivated Data Mining? Why Is It Important? • So, What Is Data Mining? • Data Mining—On What Kind of Data? • Data Mining Functionalities—What Kinds of Patterns Can Be Mined? | Reading chapter 1 |
2) | • What is data? • Attributes. • Types of attributes. • Discrete and continuous variables. • Types of data set. • Record data. • Data matrix. • Document data. • Transaction data. • Graph data. • Chemical data. • Ordered data. • Why data preprocessing? • Why is data dirty? • Why is data preprocessing important? • Multi-dimensional measure of data quality. • Major tasks in data preprocessing. • Data quality. • Noise. • Outliers. | Reading chapter 2. |
3) | • Missing values. • Duplicate data. • Mining data descriptive characteristics. • Measuring the central tendency. • Symmetric vs. skewed data. • Properties of normal distribution curve. • Histogram analysis. • Positively and negatively correlated data. • Not correlated data. • Data cleaning. • How to handle missing data? • How to handle noisy data? • Simple discretization methods: Binning. • Regression. • Cluster analysis. • Data cleaning as a process. • Aggregation. • Sampling. • Types of sampling. • Sample size. | Reading chapter 2. |
4) | • Classification. • Illustrating classification task. • Examples of classification task. • Classification techniques. • Example of a decision tree. • Another example of decision tree. • Apply model to test data. • Decision tree induction. • Issues: data preparation. • Issues: evaluating classification methods. • Algorithm for decision tree induction(ID3/C4.3). • Attribute selection measure: Information gain. • Decision tree example. | Reading chapter 8. |
5) | • Numeric variables and missing values. • Overfitting and tree pruning. • Enhancements to basic decision tree induction. • Model evaluation. • Metrics for performance evaluation. • Limitation of accuracy. • Cost matrix. • Calculation of accuracy. • Cost-sensitive measures. • Model evaluation. • Methods for performance evaluation. • Methods of estimation. • ROC (Receiver Operating Characteristic). • Instance Based Classification. • Nearest neighbor classification. • k-Nearest neighbor algorithm example. | Reading chapter 8-9. |
6) | • What is cluster analysis? • Applications of cluster analysis • What is not cluster analysis? • Notion of a cluster can be ambiguous. • Types of clustering. • Characteristics of the input data are important. • Clustering algorithms. • Hierarchical clustering. • Agglomerative clustering algorithm. • Cluster distance measures. • Single link(min) hierarchical clustering. • Single link(min) hierarchical clustering example. | Reading chapter 10. |
7) | • Complete link(max) hierarchical clustering example. • K-means clustering. • Importance of choosing initial centroids. • Limitations of k-means. • Overcoming k-means limitations. • K-means clustering example. | Reading chapter 10. |
8) | Midterm1 | |
9) | • Association rule mining • Frequent itemset • Association rule • Association rule mining task • • Apriori algorithm • Apriori algorithm example | Reading chapter 6. |
10) | • Statistical classification models. • Bayes theorem and classifier. • Bayes classifier example. • Continuous variables. | Reading chapter 6. |
11) | • Text and web mining. • Natural language processing. • Part-of-speech tagging. • Word sense disambiguation. • Text databases and IR. • Indexing techniques. • Types of text data mining. • Text classification. • Document clustering. • Text categorization. • Categorization methods. • Vector space model. | |
12) | Midterm2 | |
13) | Project presentations | |
14) | Project presentations | |
15) | Final exam |
Course Notes / Textbooks: | Data Mining Concept and Techniques J.Han and M.Kamber @2012| Morgan Kaufmann Publishers ISBN 978-0-12-381479-1 |
References: | İnternet Kaynakları |
Learning Outcomes | 1 |
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Program Outcomes | |||||||||||
1) Reaches the information in the field of power electronics and clean energy systems in depth through scientific researches; evaluates the knowledge, interprets and implements. | |||||||||||
2) Has the extensive information about current techniques and their constraints in the field of Power Electronics . | |||||||||||
3) Using limited or missing data, completes the information through scientific methods and applies; integrates the information from different disciplines. | |||||||||||
4) Aware of new and emerging applications of his/her profession; learn and examine them if needed. | |||||||||||
5) Builds the Power Electronics problems, develops methods to solve and implements innovative ways for solution. | |||||||||||
6) Develops new and/or original ideas and methods; develops innovative solutions for the design of a process, system or component. | |||||||||||
7) Designs and implements the analytical, modeling and experimental-based researches; resolves the complex situations encountered in this process and interprets. | |||||||||||
8) Leads multi-disciplinary teams, develops solution approaches to complex situations and takes responsibility. | |||||||||||
9) Uses at least one foreign language at the general level of European Language Portfolio B2 and communicates effectively in oral and written language. | |||||||||||
10) Presents the process and results of the work in national and international media systematically and clearly in written or oral language. | |||||||||||
11) Describe the social and environmental dimensions of Power Electronics Engineering applications. | |||||||||||
12) In the stages of data collection, interpretation and publication as well as all professional activities, he/she considers the social, scientific and ethical values. |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | Reaches the information in the field of power electronics and clean energy systems in depth through scientific researches; evaluates the knowledge, interprets and implements. | |
2) | Has the extensive information about current techniques and their constraints in the field of Power Electronics . | |
3) | Using limited or missing data, completes the information through scientific methods and applies; integrates the information from different disciplines. | 2 |
4) | Aware of new and emerging applications of his/her profession; learn and examine them if needed. | |
5) | Builds the Power Electronics problems, develops methods to solve and implements innovative ways for solution. | |
6) | Develops new and/or original ideas and methods; develops innovative solutions for the design of a process, system or component. | |
7) | Designs and implements the analytical, modeling and experimental-based researches; resolves the complex situations encountered in this process and interprets. | |
8) | Leads multi-disciplinary teams, develops solution approaches to complex situations and takes responsibility. | |
9) | Uses at least one foreign language at the general level of European Language Portfolio B2 and communicates effectively in oral and written language. | |
10) | Presents the process and results of the work in national and international media systematically and clearly in written or oral language. | |
11) | Describe the social and environmental dimensions of Power Electronics Engineering applications. | |
12) | In the stages of data collection, interpretation and publication as well as all professional activities, he/she considers the social, scientific and ethical values. |
Lesson | |
Project preparation |
Written Exam (Open-ended questions, multiple choice, true-false, matching, fill in the blanks, sequencing) | |
Individual Project |
Semester Requirements | Number of Activities | Level of Contribution |
Project | 1 | % 10 |
Midterms | 2 | % 50 |
Final | 1 | % 40 |
total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 60 | |
PERCENTAGE OF FINAL WORK | % 40 | |
total | % 100 |
Activities | Number of Activities | Workload |
Course Hours | 16 | 48 |
Project | 2 | 70 |
Midterms | 12 | 130 |
Final | 4 | 62 |
Total Workload | 310 |