CMP730 - PATTERN CLASSIFICATION METHODS

Course Name Code Semester Theory
(hours/week)		 Application
(hours/week)		 Credit ECTS
PATTERN CLASSIFICATION METHODS CMP730 Any Semester/Year 3 0 3 9
PrequisitesNone
Course languageEnglish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
Preparing and/or Presenting Reports
Project Design/Management
 
Instructor (s)Assist. Prof. Dr. Harun Artuner 
Course objectiveLearning of Pattern Classification Methods basic concepts. 
Learning outcomes
  1. Basics consepts of Pattern Recognition
  2. Pattern Recognition algorithms,
  3. Classification algorithms
  4. Clustering algorithms
  5. Regression algorithms
  6. Learning techniques
Course ContentClassical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition. Semi-supervised learning, combining clustering algorithms, and relevance feedback. 
ReferencesSergios Theodoridis, Pattern Recognition, Academic Press, 2008.
Christopher M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer, 2007. 

Course outline weekly

WeeksTopics
Week 1Basics consepts of Pattern Recognition
Week 2Pattern Recognition algorithms,
Week 3Classification algorithms
Week 4Clustering algorithms
Week 5Regression algorithms
Week 6Project I
Week 7Learning techniques
Week 8Learning techniques (cont.)
Week 9Case Study
Week 10Case Study (cont.)
Week 11Project II
Week 12Research presentations
Week 13Research presentations
Week 14Research presentations
Week 15Research presentations
Week 16Final exam

Assesment methods

Course activitiesNumberPercentage
Attendance1410
Laboratory00
Application00
Field activities00
Specific practical training00
Assignments00
Presentation00
Project240
Seminar115
Midterms00
Final exam135
Total100
Percentage of semester activities contributing grade succes065
Percentage of final exam contributing grade succes035
Total100

WORKLOAD AND ECTS CALCULATION

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Application000
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)14570
Presentation / Seminar Preparation13030
Project000
Homework assignment24080
Midterms (Study duration)000
Final Exam (Study duration) 14040
Total Workload32118262

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
12345
1. Graduates should have a mastery of computer science as described by the core of the Body of Knowledge.   X 
2. Graduates need understanding of a number of recurring themes, such as abstraction, complexity, and evolutionary change, and a set of general principles, such as sharing a common resource, security, and concurrency.   X  
3. Graduates of a computer science program need to understand how theory and practice influence each other.  X  
4. Graduates need to think at multiple levels of detail and abstraction.    X 
5. Students will be able to think critically, creatively and identify problems in their research.    X
6. Graduates should have been involved in at least one substantial project.     X
7. Graduates should realize that the computing field advances at a rapid pace.   X  
8. Graduates should conduct research in an ethical and responsible manner.  X   
9. Graduates should have good command of technical terms in both Turkish and English.  X   
10. Graduates should understand the full range of opportunities available in computing.    X
11. Graduates should understand that computing interacts with many different domains.     X
12. Graduates should develop the knowledge acquired at master level and apply scientific methods in order to solve scientific problems.    X 
13. Graduates should develop a complete plan of a course in computer science and teach.  X  

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest