GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION

KNOWLEDGE DISCOVERY/5581307

COURSE INFORMATION

-- (CATALOG CONTENT)

-- (TEXTBOOK)

-- (SUPPLEMENTARY TEXTBOOK)

-- (PREREQUISITES AND CO-REQUISITES)

-- LANGUAGE OF INSTRUCTION

Turkish

-- COURSE OBJECTIVES

-- COURSE LEARNING OUTCOMES

After completing this course, students will understand data mining techniques, and will have the capability to perform data mining applications.

-- MODE OF DELIVERY

The mode of delivery of this course is face to face

--WEEKLY SCHEDULE
1. Week	Principles of Data Mining
2. Week	Data Preprocessing
3. Week	Data Preprocessing
4. Week	Decision Trees
5. Week	Classifier Evaluation, Rule-Based Classifiers
6. Week	Nearest-Neighbor Classifiers, Bayesian Classifiers
7. Week	Artificial Neural Networks
8. Week	Support Vector Machines, Ensemble Models, Multiclass Problem, Class Imbalance Problem
9. Week	Apriori ve FP-Growth Algorithms
10. Week	Interestingness Measures, Sequence Pattern Mining, Multi-Level Association Rules
11. Week	k-Means Method, Hierarchical Clustering, Cluster Evaluation
12. Week	Grid and Density Based Clustering Methods,
13. Week	Model Based Clustering Methods: Expectation Maximization Algorithm and Self-Organizing Maps
14. Week	Outlier Analysis
15. Week	Text Mining
16. Week	Applications

-- TEACHING and LEARNING METHODS

-- ASSESSMENT CRITERIA

	Quantity	Total Weighting (%)
Midterm Exams	1	20
Assignment	0	0
Application	0	0
Projects	1	30
Practice	0	0
Quiz	0	0

Percent of In-term Studies		80
Percentage of Final Exam to Total Score		20

-- WORKLOAD

Activity	Total Number of Weeks	Duration (weekly hour)	Total Period Work Load
Weekly Theoretical Course Hours	15	3	45
Weekly Tutorial Hours			0
Reading Tasks	10	3	30
Searching in Internet and Library	10	3	30
Material Design and Implementation			0
Report Preparing	7	6	42
Preparing a Presentation	7	3	21
Presentation			0
Midterm Exam and Preperation for Midterm Exam	1	10	10
Final Exam and Preperation for Final Exam	1	10	10
Other (should be emphasized)			0
TOTAL WORKLOAD:			188
TOTAL WORKLOAD / 25:			7.52
Course Credit (ECTS):			7.5

-- COURSE'S CONTRIBUTION TO PROGRAM

-- NAME OF LECTURER(S)

(Assoc. Prof. Diyar Akay and other relevant faculty members)

-- WEB SITE(S) OF LECTURER(S)

(w3.gazi.edu.tr/~diyar)

-- EMAIL(S) OF LECTURER(S)

(diyar@gazi.edu.tr)

Gazi University