Course Name Code Semester T+U Hours Credit ECTS
Artificial Intelligence ENM 417 7 3 + 0 3 5
Precondition Courses
Recommended Optional Courses
Course Language Turkish
Course Level Bachelor's Degree
Course Type Optional
Course Coordinator Doç.Dr. SAFİYE SENCER
Course Lecturers Doç.Dr. SAFİYE SENCER,
Course Assistants
Course Category Field Proper Education
Course Objective Describing general structure of artificial intelligence and artificial intelligence’s algorithms, to teach artificial intelligence’s applications
Course Content Basic concepts (search, problem solving, knowledge representation methods, planning, natural language processing), artificial neural networks, expert systems, genetic algorithms, fuzzy logic.
# Course Learning Outcomes Teaching Methods Assessment Methods
1 Understanding general structure of artificial intelligence Lecture, Question-Answer, Discussion, Case Study, Testing, Performance Task,
2 Understanding artificial neural networks Lecture, Question-Answer, Discussion, Drilland Practice, Motivations to Show, Case Study, Problem Solving, Testing, Homework, Performance Task,
3 Understanding expert systems Lecture, Question-Answer, Discussion, Drilland Practice, Motivations to Show, Case Study, Problem Solving, Testing, Homework, Performance Task,
4 Understanding genetic algorithms Lecture, Question-Answer, Discussion, Drilland Practice, Motivations to Show, Case Study, Problem Solving, Testing, Homework, Performance Task,
5 Understanding fuzzy logic Lecture, Question-Answer, Discussion, Drilland Practice, Motivations to Show, Case Study, Problem Solving, Testing, Homework, Performance Task,
Week Course Topics Preliminary Preparation
1 Entrance to artificial intelligence
2 Problem solving, natural language processing
3 Knowledge representation methods
4 Planning, search, vision, robotic, agent
5 Entrance to artificial neural networks
6 Artificial neural networks (Backpropagation)
7 Artificial neural networks (LVQ network)
8 Entrance to expert systems
9 Expert systems
10 Sample of expert system
11 Entrance to genetic algorithms
12 Sample of genetic algorithms
13 Entrance to fuzzy logic
14 Sample of fuzzy logic
Resources
Course Notes
Course Resources
Order Program Outcomes Level of Contribution
1 2 3 4 5
1 Engineering graduates with sufficient knowledge background on science and engineering subjects of their related area, and who are skillful in implementing theoretical and practical knowledge for modelling and solving engineering problems. X
2 Engineering graduates with skills in identifying, describing, formulating and solving complex engineering problems, and thus,deciding and implementing appropriate methods for analyzing and modelling. X
3 Engineering graduates with skills in designing a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; for this purpose, skills in implementing modern design methods. X
4 Engineering graduates with skills in developing, selecting and implementing modern techniques and tools required for engineering applications as well as with skills in using information technologies effectively. X
5 Engineering graduates with skills in designing and conducting experiments, collecting data, analyzing and interpreting the results in order to evaluate engineering problems. X
6 Engineering graduates who are able to work within a one discipline or multi-discipline team,as well as who are able to work individually X
7 Engineering graduates who are able to effectively communicate orally and officially in Turkish Language as well as who knows at least one foreign language X
8 Engineering graduates with motivation to life-long learning and having known significance of continuous education beyond undergraduate studies for science and technology X
9 Engineering graduates with well-structured responsibilities in profession and ethics X
10 Engineering graduates having knowledge about practices in professional life such as project management, risk management and change management, and who are aware of innovation and sustainable development. X
11 Engineering graduates having knowledge about universal and social effects of engineering applications on health, environment and safety, as well as having awareness for juridical consequences of engineering solutions. X
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 55
1. Ödev 15
2. Ödev 15
3. Ödev 15
Total 100
1. Yıl İçinin Başarıya 60
1. Final 40
Total 100
ECTS - Workload Activity Quantity Time (Hours) Total Workload (Hours)
Course Duration (Including the exam week: 16x Total course hours) 16 2 32
Hours for off-the-classroom study (Pre-study, practice) 16 3 48
Mid-terms 1 12 12
Assignment 1 10 10
Performance Task (Laboratory) 1 14 14
Total Workload 116
Total Workload / 25 (Hours) 4.64
dersAKTSKredisi 5