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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
ELK 557PATTERN RECOGNITION3 + 02nd Semester7,5

COURSE DESCRIPTION
Course Level Doctorate Degree
Course Type Elective
Course Objective The primary aim of the course is enable the student to think coherently and confidently about machine learning problems, and present the student with a set of practical tools that can be applied to solve real-world problems in machine learning, coupled with an appropriate, principled approach to formulating a solution.
Course Content The course follows the basic principles of machine learning methods in showing how the different tools are developed, how they are related, how they should be deployed, and how they are used in practice. The course presents a number of methods in machine learning that are increasingly used, including Bayesian methods, and Gaussian processes.
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1The student will be able to analytically demonstrate how different models and different algorithms are related to one another.
2The student can solve pattern recognition problems by using machine learning algorithms.

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11
LO 00125444      
LO 00225444      
Sub Total410888      
Contribution25444000000

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
Mid-terms14040
Final examination14343
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