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Information
| Code | CENG708 |
| Name | Advanced Topics in Data Mining |
| Term | 2022-2023 Academic Year |
| Semester | . Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. SELMA AYŞE ÖZEL |
Course Goal / Objective
Learning advanced data mining methods and applying to advanced level problems.
Course Content
Scalable algorithms, flexible predictive modeling, web mining, text and document clustering, automated recommender systems, pattern-finding algorithms. It is assumed that every student is familiar with the basic data mining topics (clustering, classification, and association rules) and has some experience with programming and one or more data mining tools (R, RapidMiner, Weka, XLMiner, etc.).
Course Precondition
Student is expected to know basic data mining techniques and be able to implement programs about the topic.
Resources
Han, J., Kamber, M., and Pei, J., Data Mining: Concepts and Techniques, 3rd Edition, Morgan Kaufmann, 2012.
Notes
Related recent papers
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explains advanced level data mining methods. |
| LO02 | Decides which data mining technique to apply to solve a problem. |
| LO03 | Applies the advanced level data mining methods to solve problems. |
| LO04 | Develops a data mining application that can solve a real life problem. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | On the basis of the competencies gained at the undergraduate level, it has an advanced level of knowledge and understanding that provides the basis for original studies in the field of Computer Engineering. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | By reaching scientific knowledge in the field of engineering, he/she reaches the knowledge in depth and depth, evaluates, interprets and applies the information. | 4 |
| PLO03 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the new and developing practices of his / her profession and examining and learning when necessary. | 3 |
| PLO04 | Yetkinlikler - Öğrenme Yetkinliği | Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. | 3 |
| PLO05 | Yetkinlikler - Öğrenme Yetkinliği | Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process. | 3 |
| PLO06 | Yetkinlikler - Öğrenme Yetkinliği | Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design. | 1 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Has the skills of learning. | |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Being aware of new and emerging applications of Computer Engineering examines and learns them if necessary. | 2 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Transmits the processes and results of their studies in written or oral form in the national and international environments outside or outside the field of Computer Engineering. | 1 |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Has comprehensive knowledge about current techniques and methods and their limitations in Computer Engineering. | 2 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Uses information and communication technologies at an advanced level interactively with computer software required by Computer Engineering. | 3 |
| PLO12 | Bilgi - Kuramsal, Olgusal | Observes social, scientific and ethical values in all professional activities. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Overview of basic data mining topics | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 2 | Advanced pattern mining algorithms | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 3 | Sequence mining algorithms | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 4 | Advanced classification algorithms (Support vector machines, Baysian Belief Networks) | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 5 | Classificaiton by backpropoagation, other methods | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 6 | Advanced clustering methods (Probabilistic model-based clustering) | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 7 | Clustering high dimensional data | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 8 | Mid-Term Exam | Reading of course notes, literature review | Ölçme Yöntemleri: Sözlü Sınav, Ödev |
| 9 | Clustering graph and network data | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 10 | Outlier detection methods | Reading of course notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 11 | Data mining trends (web mining) | Literature survey | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 12 | Text mining, Recommender systems | Literature survey | Öğretim Yöntemleri: Tartışma, Bireysel Çalışma |
| 13 | Social network analysis | Literature survey | Öğretim Yöntemleri: Tartışma, Bireysel Çalışma |
| 14 | Paper presentations | Literature survey, preparing presentation | Öğretim Yöntemleri: Bireysel Çalışma, Örnek Olay, Soru-Cevap |
| 15 | Project presentations | Preparing project and its presentation | Öğretim Yöntemleri: Proje Temelli Öğrenme , Tartışma, Soru-Cevap |
| 16 | Writing the project report | Preparing the project report | Ölçme Yöntemleri: Sözlü Sınav, Proje / Tasarım |
| 17 | Term Exams | Submission of the project report | Ölçme Yöntemleri: Proje / Tasarım, Sözlü Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||