Ders Bilgileri

Ders Tanımı

Ders Kodu Yarıyıl T+U Saat Kredi AKTS
VISION SYSTEMS I ELE 429 7 3 + 0 3 5
 Dersin Dili Türkçe Dersin Seviyesi Lisans Dersin Türü SECMELI Dersin Koordinatörü Doç.Dr. ÖZDEMİR ÇETİN Dersi Verenler Dersin Yardımcıları Dersin Kategorisi Dersin Amacı Recently, many applications based on manufacturing includes pattern recognition techniques. Industrial part, fingerprint, signature, face, iris and retina recognition are some of them.At the end of this course, it is aimed that the students should understand pattern recognition concepts and to design a pattern recognition system for any kind of application. They also should know the problem solution steps of a pattern recognition system. Dersin İçeriği The definition of the patterns and basic concepts. Pattern classes. Feature extraction. Pattern classification techniques. Statistical pattern classification. Introduction to machine learning. Pattern classification using machine learning. Learning types in machine learning. Performance analysis in pattern recognition. Sample applications (Fingerprint, industrial part recognition, signature and character recognition)
 Dersin Öğrenme Çıktıları Öğretim Yöntemleri Ölçme Yöntemleri 1 - Understand the fundamentals of pattern recognition 1 - 2 - A - 2 - Comprehend actual pattern recognition applications 1 - 2 - A - 3 - Comprehend pattern classification methods 1 - A - B - 4 - Design a pattern recognition system 1 - A - B -
 Öğretim Yöntemleri: 1:Lecture 2:Question-Answer Ölçme Yöntemleri: A:Testing B:Oral Exam

Ders Akışı

Hafta Konular ÖnHazırlık
1 The definition of the pattern, basic concepts, pattern classes
2 Feature vectors.
3 Pattern classification techniques, statistical pattern classification.
4 Statistical pattern classification
5 The prediction of the probabilistic density functions
6 Bayesian decision theory, maximum likelihood theory
7 Introduction to machine learning. Supervised, unsupervised and reinforced learning
8 Pattern recognition based on machine learning.
9 Error analysis on the classification
10 Reliability on the classification
11 Design of a sample pattern recognition system
12 The software and hardware architecture of a pattern recognition system, sensors
13 Sample applications and presentations by students.
14 Sample applications and presentations by students.

Ders Notu
Ders Kaynakları

Dersin Program Çıktılarına Katkısı

No Program Öğrenme Çıktıları KatkıDüzeyi
1 2 3 4 5
1 To have latest knowledge and skills intended for research and practice in electronic technology X
2 To utilize equipments and instruments used in electronic technology X
3 To develop curriculum related to electronic technology and have a skill to transfer those accumulation by oral and written way X
4 To have knowledge and skills for planning, designing and managing procedures independently or in cooperation X
5 To have an open mind to ethic auditing and positive criticism, and have a constructive and interpreting attitude against scientific and social problems
6 To disseminate and realize the environmental awareness
7 To cooperate with social organizations and the society
8 To contribute to the education of people who work under his/her responsibility and to manage some activities for their vocational careers and social rights
9 To appropriate self learning and life- long learning principles
10 To congregate in national or international scale to see individual applications on the premises and to perform some activities and mobility for professional advancement in electronic technology

Değerlendirme Sistemi

YARIYIL İÇİ ÇALIŞMALARI SIRA KATKI YÜZDESİ
AraSinav 1 50
KisaSinav 1 50
Toplam 100
Yıliçinin Başarıya Oranı 50
Finalin Başarıya Oranı 50
Toplam 100

AKTS - İş Yükü

Etkinlik Sayısı Süresi(Saat) Toplam İş yükü(Saat)
Course Duration (Including the exam week: 16x Total course hours) 16 3 48
Hours for off-the-classroom study (Pre-study, practice) 16 3 48
Mid-terms 1 10 10
Assignment 1 5 5
Performance Task (Laboratory) 1 15 15
Toplam İş Yükü 126
Toplam İş Yükü /25(s) 5.04
Dersin AKTS Kredisi 5.04
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