1	Course Title:	PATTERN RECOGNITION
2	Course Code:	BMB4018
3	Type of Course:	Optional
4	Level of Course:	First Cycle
5	Year of Study:	4
6	Semester:	8
7	ECTS Credits Allocated:	5
8	Theoretical (hour/week):	3
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	0
11	Prerequisites:	None
12	Recommended optional programme components:	None
13	Language:	Turkish
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Prof. Dr. Ahmet Emir DİRİK
16	Course Lecturers:
17	Contactinformation of the Course Coordinator:	Ahmet Emir Dirik, edirik@uludag.edu.tr
18	Website:
19	Objective of the Course:	The main objectives of the course are as follows: To provide essential knowledge of pattern recognition fundamentals. To develop advanced practical skills and competency in pattern recognition. To apply these skills to the full spectrum of pattern recognition applications, through independent research and investigation. To develop the students' transferable skills including communication (oral, written and aural), time and project management.
20	Contribution of the Course to Professional Development	To be able to follow innovations and apply them in the field by using the competence of collecting information, researching and analyzing them.

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Learning Outcomes:

1 Gain sufficient knowledge on pattern recognition; the ability to model and solve computer vision application problems using theoretical and practical knowledge. ; 2 Gain the ability to identify, model, and solve complex problems; the ability to select and apply appropriate analysis and modeling methods for these problems. ;

22	Course Content:

Week	Theoretical	Practical
1	Introduction to Pattern Recognition
2	Bayesian decision theory
3	Bayesian decision theory (continued)
4	Bayesian estimation
5	Bayesian estimation (continued)
6	Feature selection and extraction
7	Linear Discriminant Functions
8	Nonparametric Pattern Recognition
9	Algorithm-independent Learning
10	Comparing classifiers
11	Learning with Multiple Algorithms
12	Syntactic Pattern Recognition
13	Project presentations
14	Review

23	Textbooks, References and/or Other Materials:	Pattern Recognition and Machine Learning, C. M. Bishop, 2006, Springer
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	1	40
Quiz	0	0
Homeworks, Performances	0	0
Final Exam	1	60
Total	2	100
Contribution of Term (Year) Learning Activities to Success Grade		40
Contribution of Final Exam to Success Grade		60
Total		100
Measurement and Evaluation Techniques Used in the Course	Measurement and evaluation is carried out according to the principles of Bursa Uludag University Associate and Undergraduate Education Regulation.
Information

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ECTS / WORK LOAD TABLE

Activites	NUMBER	TIME [Hour]	Total WorkLoad [Hour]
Theoretical	14	3	42
Practicals/Labs	0	0	0
Self Study and Preparation	14	2	28
Homeworks, Performances	0	0	0
Projects	1	30	30
Field Studies	0	0	0
Midtermexams	1	18	18
Others	0	0	0
Final Exams	1	32	32
Total WorkLoad			168
Total workload/ 30 hr			5
ECTS Credit of the Course			5

26	CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS
PQ1 PQ2 PQ3 PQ4 PQ5 PQ6 PQ7 PQ8 PQ9 PQ10 PQ11 PQ12 LO1 5 0 0 0 0 0 0 0 0 0 0 0 LO2 0 5 0 0 0 0 0 0 0 0 0 0

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High