ARCHITECTURE | |||||
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 | 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) | Using the theoretical/conceptual and practical knowledge acquired for architectural design, design activities and research. | |
2) | Identifying, defining and effectively discussing aesthetic, functional and structural requirements for solving design problems using critical thinking methods. | |
3) | Being aware of the diversity of social patterns and user needs, values and behavioral norms, which are important inputs in the formation of the built environment, at local, regional, national and international scales. | |
4) | Gaining knowledge and skills about architectural design methods that are focused on people and society, sensitive to natural and built environment in the field of architecture. | |
5) | Gaining skills to understand the relationship between architecture and other disciplines, to be able to cooperate, to develop comprehensive projects; to take responsibility in independent studies and group work. | |
6) | Giving importance to the protection of natural and cultural values in the design of the built environment by being aware of the responsibilities in terms of human rights and social interests. | |
7) | Giving importance to sustainability in the solution of design problems and the use of natural and artificial resources by considering the social, cultural and environmental issues of architecture. | |
8) | Being able to convey and communicate all kinds of conceptual and practical thoughts related to the field of architecture by using written, verbal and visual media and information technologies. | |
9) | Gaining the ability to understand and use technical information about building technology such as structural systems, building materials, building service systems, construction systems, life safety. | |
10) | Being aware of legal and ethical responsibilities in design and application processes. |