BİLGİ PAKETİ / DERS KATALOĞU
1410002018 Machine Vision
Degree Programs
COMPUTER ENGINEERING
Diploma SupplementNational Qualifications
COMPUTER ENGINEERING
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Ders Genel Tanıtım Bilgileri

Course Code: 1410002018
Ders İsmi: Machine Vision
Ders Yarıyılı: Spring
Ders Kredileri:
Theoretical Practical Credit ECTS
3 0 3 5
Language of instruction: TR
Ders Koşulu:
Ders İş Deneyimini Gerektiriyor mu?: No
Type of course: Bölüm Seçmeli
Course Level:
Bachelor TR-NQF-HE:6. Master`s Degree QF-EHEA:First Cycle EQF-LLL:6. Master`s Degree
Mode of Delivery: Face to face
Course Coordinator : Dr.Öğr.Üyesi Recep DURANAY
Course Lecturer(s):
Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives: The course aims to provide students with information on basic methods and approaches in the field of machine learning and to enable students to apply machine learning methods to practical problems.
Course Content: Basic concepts and approaches of machine learning. Guided machine learning methods. Concept learning and learning with decision trees. Bayes' theorem-based approaches in machine learning. Evolutionary approach and genetic programming. Artificial neural networks, learning with support vectors and reinforcement learning. Undirected learning methods and classification.

Learning Outcomes

The students who have succeeded in this course;
Learning Outcomes
1 - Knowledge
Theoretical - Conceptual
1) Knowledge of basic methods in machine learning.
2 - Skills
Cognitive - Practical
1) Ability to model and solve practical problems using machine learning methods
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Ders Akış Planı

Week Subject Related Preparation
1) Introduction to Machine Learning Coursebook / lecture Notes Notes
2) Concept Learning Coursebook / Lecture Notes
3) Learning with Decision Trees Coursebook / Lecture Notes
4) Genetic Algorithms and Genetic Programming Coursebook / Lecture Notes
5) Project Proposal Submissions Coursebook / Lecture Notes
6) Learning with the Bayesian Approach Coursebook / Lecture Notes
7) Bayesian Belief Networks Coursebook / Lecture Notes
8) Midterm
9) Regression and Correlation Coursebook / Lecture Notes
10) Regression and Correlation Coursebook / Lecture Notes
11) Support Vector Machines Coursebook / Lecture Notes
12) Support Vector Machines Coursebook / Lecture Notes
13) Deep learning Coursebook / Lecture Notes
14) Deep Vision Coursebook / Lecture Notes
15) Deep Vision Coursebook / Lecture Notes
16) Final

Sources

Course Notes / Textbooks: Ders Notları - Lecture Notes
References: Ders Notları - Lecture Notes

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları

1

2
Program Outcomes
1) PO 1.1) Sufficient knowledge in mathematics, science and computer engineering
2) PO 1.2) Ability to apply theoretical and applied knowledge in mathematics, science and computer engineering for modeling and solving engineering problems.
3) PO 2.1) Identifying complex engineering problems
4) PO 2.2) Defining complex engineering problems
5) PO 2.3) Formulating complex engineering problems
6) PO 2.4) Ability to solve complex engineering problems
7) PO 2.5) Ability to choose and apply appropriate analysis and modeling methods
8) PO 3.1) Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions.
9) PO 3.2) Ability to apply modern design methods under realistic constraints and conditions for a complex system, process, device or product
10) PO 4.1) Developing modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications
11) PO 4.2) Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications
12) PO 4.3) Ability to use information technologies effectively.
13) PO 5.1) Examination of complex engineering problems or discipline-specific research topics, designing experiments
14) PO 5.2) Examination of complex engineering problems or discipline-specific research topics, experimentation
15) PO 5.3 ) Analysis of complex engineering problems or discipline-specific research topics, data collection
16) PO 5.4) Analyzing the results of complex engineering problems or discipline-specific research topics
17) PO 5.5) Examining and interpreting complex engineering problems or discipline-specific research topics

Ders - Öğrenme Kazanımı İlişkisi

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) PO 1.1) Sufficient knowledge in mathematics, science and computer engineering
2) PO 1.2) Ability to apply theoretical and applied knowledge in mathematics, science and computer engineering for modeling and solving engineering problems.
3) PO 2.1) Identifying complex engineering problems
4) PO 2.2) Defining complex engineering problems
5) PO 2.3) Formulating complex engineering problems
6) PO 2.4) Ability to solve complex engineering problems
7) PO 2.5) Ability to choose and apply appropriate analysis and modeling methods
8) PO 3.1) Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions.
9) PO 3.2) Ability to apply modern design methods under realistic constraints and conditions for a complex system, process, device or product 5
10) PO 4.1) Developing modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications
11) PO 4.2) Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications
12) PO 4.3) Ability to use information technologies effectively.
13) PO 5.1) Examination of complex engineering problems or discipline-specific research topics, designing experiments
14) PO 5.2) Examination of complex engineering problems or discipline-specific research topics, experimentation
15) PO 5.3 ) Analysis of complex engineering problems or discipline-specific research topics, data collection
16) PO 5.4) Analyzing the results of complex engineering problems or discipline-specific research topics
17) PO 5.5) Examining and interpreting complex engineering problems or discipline-specific research topics

Öğrenme Etkinliği ve Öğretme Yöntemleri

Course
Problem Çözme

Ölçme ve Değerlendirme Yöntemleri ve Kriterleri

Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama)
Homework

Assessment & Grading

Semester Requirements Number of Activities Level of Contribution
Homework Assignments 2 % 30
Midterms 1 % 30
Semester Final Exam 1 % 40
total % 100
PERCENTAGE OF SEMESTER WORK % 60
PERCENTAGE OF FINAL WORK % 40
total % 100

İş Yükü ve AKTS Kredisi Hesaplaması

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Study Hours Out of Class 14 6 84
Homework Assignments 2 10 20
Midterms 1 2 2
Final 1 3 3
Total Workload 151