PERFORMING ARTS | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
CMP4336 | Introduction to Data Mining | Spring Fall |
3 | 0 | 3 | 6 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Language of instruction: | English |
Type of course: | Non-Departmental Elective |
Course Level: | Bachelor’s Degree (First Cycle) |
Mode of Delivery: | Face to face |
Course Coordinator : | Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR |
Recommended Optional Program Components: | None |
Course Objectives: | In this course, data mining algorithms and computational paradigms that are used to extract useful knowledge, extract patterns and regularities in databases, and perform prediction and forecasting will be discussed. Supervised and unsupervised learning approaches will be covered with a focus on pattern discovery and cluster analysis. |
The students who have succeeded in this course; 1. Be able to understand Data Gathering and Pre-processing 2. Become familiar with Frequent Item Set Detection 3. Be able to understand Association Rule Mining 4. Be able to understand Classifiers, and their benefits 5. Be able to use Clustering 6. Be able to understand Clustering Evaluation |
1.Introduction to Basic Concepts 2.Data Exploration 3.Classification 4.Clustering 5.Dimensionality Reduction 6.Frequent Item Set Mining 7.Association Rule Mining |
Week | Subject | Related Preparation |
1) | Introduction to Basic Concepts | None |
2) | Data Exploration: Summary Statistics, Visualization, OLAP and Multi-dimensional Data Analysis | None |
3) | Data Pre-Processing, Transformation, Normalization, Standardization | None |
4) | Classification and Regression: Model Selection and Generalization, Decision Trees, Performance Evaluation | None |
5) | Classification: Bayesian Decision Theory, Parametric Classification, Naive Bayes Classifier, Instance-Based Classifiers | |
6) | Classification | None |
6) | Classification and Regression: Artificial Neural Networks, Support Vector Machines | |
7) | Midterm I | Review of all topics covered so far |
8) | Clustering: Partitioning and Hierarchical Algorithms | None |
9) | Clustering: Density-Based Algorithms | |
10) | Cluster Evaluation, Comparing Clusterings | None |
11) | Midterm II | none |
12) | Dimensionality Reduction | none |
13) | Frequent Item Set Mining | none |
14) | Association Rule Mining | none |
Course Notes / Textbooks: | Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach and Vipin Kumar |
References: | Data Mining: Concepts and Techniques, by Jiawei Han, Micheline Kamber and Jian Pei |
Semester Requirements | Number of Activities | Level of Contribution |
Homework Assignments | 2 | % 20 |
Project | 1 | % 20 |
Midterms | 2 | % 20 |
Final | 1 | % 40 |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 40 | |
PERCENTAGE OF FINAL WORK | % 60 | |
Total | % 100 |
Activities | Number of Activities | Workload |
Course Hours | 14 | 42 |
Study Hours Out of Class | 16 | 32 |
Project | 5 | 15 |
Homework Assignments | 6 | 12 |
Midterms | 8 | 28 |
Final | 6 | 26 |
Total Workload | 155 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | They acquire theoretical, historical and aesthetic knowledge specific to their field by using methods and techniques related to performing arts (acting, dance, music, etc.). | 2 |
2) | They have knowledge about art culture and aesthetics and they provide the unity of theory and practice in their field. | 2 |
3) | They are aware of national and international values in performing arts. | 2 |
4) | Abstract and concrete concepts of performing arts; can transform it into creative thinking, innovative and original works. | 1 |
5) | They have the sensitivity to run a business successfully in their field. | 3 |
6) | Develops the ability to perceive, think, design and implement multidimensional from local to universal. | 3 |
7) | They have knowledge about the disciplines that the performing arts field is related to and can evaluate the interaction of the sub-disciplines within their field. | 2 |
8) | They develop the ability to perceive, design, and apply multidimensionality by having knowledge about artistic criticism methods. | 3 |
9) | They can share original works related to their field with the society and evaluate their results and question their own work by using critical methods. | 1 |
10) | They follow English language resources related to their field and can communicate with foreign colleagues in their field. | 1 |
11) | By becoming aware of national and international values in the field of performing arts, they can transform abstract and concrete concepts into creative thinking, innovative and original works. | 3 |
12) | They can produce original works within the framework of an interdisciplinary understanding of art. | 2 |
13) | Within the framework of the Performing Arts Program and the units within it, they become individuals who are equipped to take part in the universal platform in their field. | 3 |
14) | Within the Performing Arts Program, according to the field of study; have competent technical knowledge in the field of acting and musical theater. | 2 |
15) | They use information and communication technologies together with computer software that is at least at the Advanced Level of the European Computer Use License as required by the field. | 3 |