GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
KNOWLEDGE DISCOVERY/5581307
Course Title: KNOWLEDGE DISCOVERY
Credits 3 ECTS 7.5
Course Semester 1 Type of The Course Elective
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
NO
 PROGRAM LEARNING OUTCOMES
1
2
3
4
5
1Can Reach the information in width and in depth by conducting scientific research in the field, evaluate, interpret and apply the information.X
2Has comprehensive knowledge about current techniques and methods applied in engineering and their limitations.X
3Completes and applies knowledge using scientific methods, using uncertain, limited or incomplete data; use information from different disciplines together.X
4Aware of the new and emerging practices of the profession, examines and learns when needed.X
5Defines and formulates problems related to the field, develops methods to solve them and applies innovative methods in solutions.X
6Develops new and / or original ideas and methods; design complex systems or processes and develop innovative / alternative solutions in their designs.X
7Designs and applies theoretical, experimental and modeling based research; examines and solves the complex problems encountered in this process.X
8Can work effectively in disciplinary and multidisciplinary teams, can lead such teams and develop solutions in complex situations; work independently and take responsibility.X
9Communicate verbally and in writing by using a foreign language at least at the B2 level of European Language Portfolio.X
10Transfer the process and results of his / her studies in written and verbal form in a systematic and clear manner in national and international environments within or outside the field.X
11Knows the social, environmental, health, security, legal aspects of engineering applications as well as project management and business practices and is aware of the constraints that these impose on engineering applications.X
12It considers social, scientific and ethical values in the stages of data collection, interpretation and announcement and in all professional activities.X
 -- 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)