| Semester | Course Unit Code | Course Unit Title | T+P+L | Credit | Number of ECTS Credits |
| 1 | ECE663 | PATTERN RECOGNITION | 3+0+0 | 3 | 7,5 |
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Language of Instruction
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English
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Level of Course Unit
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Master's Degree
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Department / Program
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ELECTRICAL AND COMPUTER ENGINEERING
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Type of Program
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Formal Education
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Type of Course Unit
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Elective
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Course Delivery Method
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Face To Face
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Objectives of the Course
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Gain an understanding of pattern recognition methods
Learn the techniques used for developing pattern recognition models
Develop skills for practical aspects of deep learning
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Course Content
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This course provides an introduction to pattern recognition. It covers Bayesian and frequentist statistics, Bayesian learning methods, decision theory, generalized linear models and the exponential family, and regression models. Mathematical principles will be explained to provide a solid foundation for pattern recognition. Methods will be implemented by a software and applied on various pattern recognition problems.
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Course Methods and Techniques
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Prerequisites and co-requisities
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None
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Course Coordinator
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None
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Name of Lecturers
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None
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Assistants
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None
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Work Placement(s)
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No
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Recommended or Required Reading
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Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
ECTS Allocated Based on Student Workload
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
Weekly Detailed Course Contents
Contribution of Learning Outcomes to Programme Outcomes
Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant
https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77912&lang=en