GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
PATTERN RECOGNITION/5121305
Course Title: PATTERN RECOGNITION
Credits 3 ECTS 8
Semester 1 Compulsory/Elective Elective
COURSE INFO
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- NAME OF LECTURER(S)
  Assoc.Prof.Dr. Hasan Şakir BİLGE
 -- WEB SITE(S) OF LECTURER(S)
  w3.gazi.edu.tr/~bilge
 -- EMAIL(S) OF LECTURER(S)
  bilge@gazi.edu.tr
 -- LEARNING OUTCOMES OF THE COURSE UNIT
Applying of classification methods in a sample problem successfully
Obtaining the ability of effective use of feature selection and dimensionality reduction
Understanding that pattern recognition can be applied to different problems in a similar way
 -- MODE OF DELIVERY
  The mode of delivery of this course is face to face.
 -- PREREQUISITES AND CO-REQUISITES
  There is no prerequisite or co-requisite for this course.
 -- RECOMMENDED OPTIONAL PROGRAMME COMPONENTS
  There is no recommended optional programme component for this course.
 --COURSE CONTENT
1. Week  General introduction
2. Week  Classifiers based on Bayes decision theory
3. Week  Linear classifiers
4. Week  Linear discriminant functions
5. Week  Non linear classifiers
6. Week  Support vector machines
7. Week  Support vector machines
8. Week  Feature extraction
9. Week  Feature extraction
10. Week  Linear transformations
11. Week  Feature selection
12. Week  Feature selection
13. Week  Dimensionality reduction
14. Week  Clustering
15. Week  Clustering
16. Week  Project presentations
 -- RECOMMENDED OR REQUIRED READING
  1. Pattern Recognition, S. Theodoridis, K. Koutroumbas, Academic Press. 2. Pattern Classification, R.O. Duda, P.E. Hart, D.G. Stork, Wiley.
 -- PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
  Lecture, Question & Answer, Practice
 -- WORK PLACEMENT(S)
  NO
 -- ASSESSMENT METHODS AND CRITERIA
 
Quantity
Percentage
 Mid-terms
0
0
 Assignment
9
30
 Exercises
1
30
 Projects
1
30
 Practice
1
10
 Quiz
0
0
 Contribution of In-term Studies to Overall Grade  
40
 Contribution of Final Examination to Overall Grade  
60
 -- WORKLOAD
 Efficiency  Total Week Count  Weekly Duration (in hour)  Total Workload in Semester
 Theoretical Study Hours of Course Per Week
14
3
42
 Practising Hours of Course Per Week
0
 Reading
14
1
14
 Searching in Internet and Library
14
1
14
 Designing and Applying Materials
14
1
14
 Preparing Reports
1
30
30
 Preparing Presentation
14
1
14
 Presentation
2
14
28
 Mid-Term and Studying for Mid-Term
0
 Final and Studying for Final
1
32
32
 Other
0
 TOTAL WORKLOAD: 
188
 TOTAL WORKLOAD / 25: 
7.52
 ECTS: 
8
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
 PROGRAM LEARNING OUTCOMES
1
2
3
4
5
1Improves and deepens the field knowledge at an expert level based on undergraduate proficiency.X
2Comprehends the interactions between the computer science and other related disciplines.X
3Uses expert level theoretical and practical knowledge acquired in the computer science field.X
4Creates new knowledge by integrating the computer science knowledge and the knowledge from related disciplines.X
5Defines a problem in the computer science field.X
6Analyses the problems in the computer science field by using scientific research methods.X
7Proposes solutions to the problems in the computer science field.X
8Solves problems in the computer science field.X
9Evaluates the results within the perspectives of quality processes.X
10Develops new approaches and methods by taking responsibility in complex situations in the application stages.X