CIVIL ENGINEERING | |||||
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 | Fall 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) | Adequate knowledge in mathematics, science and civil engineering; the ability to use theoretical and practical knowledge in these areas in complex engineering problems. | |
2) | Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | |
3) | Ability to design a complex system, process, structural and/or structural members to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose. | |
4) | Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in civil engineering applications; ability to use civil engineering technologies effectively. | |
5) | Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or civil engineering research topics. | |
6) | Ability to work effectively within and multi-disciplinary teams; individual study skills. | |
7) | Ability to communicate effectively in English and Turkish (if he/she is a Turkish citizen), both orally and in writing. | |
8) | Awareness of the necessity of lifelong learning; ability to access information to follow developments in civil engineering technology. | |
9) | To act in accordance with ethical principles, professional and ethical responsibility; having awareness of the importance of employee workplace health and safety. | |
10) | Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development. | |
11) | Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of civil engineering solutions. |