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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
CENG 525PATTERN RECOGNITION3 + 07th Semester5

COURSE DESCRIPTION
Course Level Bachelor'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
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11PO 12
LO 001      333333
LO 002      434344
LO 003      545454
Sub Total      121012101211
Contribution000000434344

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)14342
Assignments21020
Mid-terms11010
Final examination11616
Total Work Load

ECTS Credit of the Course






130

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