Course Name Code Semester T+U Hours Credit ECTS
Machine Learning and Computer Vision Application BSM 512 0 3 + 0 3 6
Precondition Courses
Recommended Optional Courses
Course Language Turkish
Course Level yuksek_lisans
Course Type Optional
Course Coordinator Dr.Öğr.Üyesi SERAP ÇAKAR
Course Lecturers
Course Assistants
Course Category
Course Objective Introduction to machine learning. Learning the terms and concepts. Construction and encoding of data.
Course Content Evaluation of hypothesis. Learning at artificial neural network and mixed systems. Productivity of learning and error analysis methods. Increasing the reliability at machine learning. Pattern recognition and classification systems. Feature extraction methods. Feature vectors and classification designs at signature, fingerprint, object recognition etc. Example applications about machine learning.
# Course Learning Outcomes Teaching Methods Assessment Methods
1 Learn teoric and technic basics of using artifical intelligence with electronic devices and mechanics Lecture, Question-Answer, Drilland Practice, Problem Solving, Project Based Learning, Testing, Homework, Project / Design,
2 Learn to design classificition system with neural networks. Lecture, Question-Answer, Drilland Practice, Problem Solving, Project Based Learning, Testing, Homework, Project / Design,
3 Learn to code and config Lecture, Question-Answer, Drilland Practice, Project Based Learning, Testing, Homework, Project / Design,
Week Course Topics Preliminary Preparation
Course Notes
Course Resources
Order Program Outcomes Level of Contribution
1 2 3 4 5
1 ability to access wide and deep information with scientific researches in the field of Engineering, evaluate, interpret and implement the knowledge gained in his/her field of study X
2 ability to complete and implement “limited or incomplete data” by using the scientific methods. X
3 ability to consolidate engineering problems, develop proper method(s) to solve and apply the innovative solutions to them X
4 ability to develop new and original ideas and method(s), to develop new innovative solutions at design of system, component or process X
5 gain comprehensive information on modern techniques, methods and their borders which are being applied to engineering X
6 ability to design and apply analytical, modelling and experimental based research, analyze and interpret the faced complex issues during the design and apply process X
7 gain high level ability to define the required information and data X
8 ability to work in multi-disciplinary teams and to take responsibility to define approaches for complex situations X
9 systematic and clear verbal or written transfer of the process and results of studies at national and international environments X
10 aware of social, scientific and ethical values guarding adequacy at all professional activities and at the stage of data collection, interpretation and announcement X
11 aware of new and developing application of profession and ability to analyze and study on those applications X
12 ability to interpret engineering application’s social and environmental dimensions and it’s compliance with the social environment X
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 100
Total 100
1. Yıl İçinin Başarıya 50
1. Final 50
Total 100
ECTS - Workload Activity Quantity Time (Hours) Total Workload (Hours)
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 20 20
Final examination 1 25 25
Total Workload 141
Total Workload / 25 (Hours) 5.64
dersAKTSKredisi 6