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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
CENG 420MACHINE LEARNING & PATTERN RECOGNITION3 + 06th Semester5

COURSE DESCRIPTION
Course Level Bachelor's Degree
Course Type Elective
Course Objective Purpose of this course is to teach fundamental concepts of machine learning and pattern recognition.
Course Content Introduction to machine learning, supervised learning, regression, model order and generalization properties, Bayes decision theory, maximum likelihood method, distance functions, multivariable models and regression, dimensionality reduction and principal component analysis, k-means clustering, decision trees, support vector machines, artificial neural networks and hidden Markov models.
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1Lists basic pattern recognition concepts
2Explains statistical methods
3Explains linear methods
4Explains nonlinear methods

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11PO 12
LO 001121121215423
LO 002212212144522
LO 003212541254325
LO 004212423254255
Sub Total757129771517141115
Contribution212322244434

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)14456
Mid-terms11515
Final examination11717
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