1. SECOND CYCLE - MASTER'S DEGREE
  2. THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES
  3. COMPUTER ENGINEERING DEPARTMENT
  4. 1281 Computer Engineering
Course Information Course Learning Outcomes Course's Contribution To Program ECTS Workload Course Details
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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
CENG 525PATTERN RECOGNITION3 + 02nd Semester7,5

COURSE DESCRIPTION
Course Level Master's Degree
Course Type Elective
Course Objective The main objective of this course is teaching students the pattern recognition methods.
Course Content Introduction to pattern recognition, discrete events and Bayes rule, expected loss, Bayes risk, Gaussian decision functions, error bounds, noisy features, ML parameter estimation, principal component analysis (PCA), eigen faces, non parametric estimation, k-NN prediction, linear discriminant analysis
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1Select the most appropriate method for applying to a given object or data
2Analyze the pattern recognition methods and the results
3Comprehend the effects of noise on the data that is obtained from real-life and extract patterns from these noisy data

COURSE'S CONTRIBUTION TO PROGRAM
Data not found.

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Hours for off-the-classroom study (Pre-study, practice)14570
Assignments5840
Mid-terms11515
Final examination12828
Total Work Load

ECTS Credit of the Course





195

7,5
COURSE DETAILS
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L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes