Manufacturing Execution Systems Operator

Course Details

KTO KARATAY UNIVERSITY
Trade and Industry Vocational School
Programme of Manufacturing Execution Systems Operator
Course Details

Course Code	Course Name	Year	Period	Semester	T+A+L	Credit	ECTS
07821111	Artificial Intelligence and Machine Learning	1	Spring	2	2+2+0	5	5

Course Type	Elective
Course Cycle	Associate (Short Cycle) (TQF-HE: Level 5 / QF-EHEA: Short Cycle / EQF-LLL: Level 5)
Course Language	Turkish
Methods and Techniques	Lectures, Q&A sessions, discussions, group work, problem solving, and practical laboratory activities.
Mode of Delivery	Face to Face
Prerequisites	None; basic mathematics and programming knowledge are recommended.
Coordinator	Lect. Mehmet AKSOY
Instructor(s)	Lect. Yasin BÜYÜKER
Instructor Assistant(s)	-

Course Instructor(s)

Name and Surname	Room	E-Mail Address	Internal	Meeting Hours
Lect. Yasin BÜYÜKER	T-219	[email protected]	7408	Tuesday 14:00/16:00

Course Content

The course covers the fundamental concepts, history, and applications of artificial intelligence. It introduces core AI methods such as knowledge representation, inference mechanisms, expert systems, fuzzy logic, genetic algorithms, and multi-agent systems. Machine learning paradigms (supervised, unsupervised, and reinforcement learning), data preprocessing, model selection, training, and evaluation are examined through practical examples. Students explore the design and application of AI solutions across various industries.

Objectives of the Course

The aim of this course is to provide students with an understanding of the concepts, principles, and applications of Artificial Intelligence (AI) and Machine Learning (ML). Students gain fundamental knowledge and skills in data-driven decision making, developing learning systems, and applying AI techniques to industrial and scientific problems.

Contribution of the Course to Field Teaching

Basic Vocational Courses
Specialization / Field Courses	X
Support Courses
Transferable Skills Courses
Humanities, Communication and Management Skills Courses

Relationships between Course Learning Outcomes and Program Outcomes

Relationship Levels
Lowest	Low	Medium	High	Highest
1	2	3	4	5

#	Program Learning Outcomes	Level
P4	Effectively uses information technologies (software, programs, animation, etc.) related to their profession	5
P5	Possesses the ability to independently evaluate professional problems and issues using an analytical and critical approach and to propose solutions.	4
P11	Has the ability to define and design manufacturing operations and processes.	3

Course Learning Outcomes

Upon the successful completion of this course, students will be able to:
No	Learning Outcomes	Outcome Relationship	Measurement Method **
O1	Knows the basic concepts of artificial intelligence and machine learning.	P.4.12
O2	Knows machine learning modeling processes.	P.4.13
O3	Knows the data set pre-processing processes.	P.4.14
O4	Can perform data types and data type conversions.	P.4.15
O5	Explains the relationship between artificial intelligence and machine learning and other disciplines.	P.5.2
O6	Knows the usage areas of artificial intelligence and machine learning.	P.5.3
O7	Machine learning can perform model selection.	P.5.4
O8	Knows the processes of data collection and data set creation.	P.11.5
** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7

Weekly Detailed Course Contents

Week	Topics
1	Concept, history, and fundamental approaches of artificial intelligence
2	Introduction to machine learning: basic concepts and components
3	Types and application areas of artificial intelligence
4	Expert systems, knowledge representation, and inference mechanisms
5	Fundamentals of fuzzy logic and genetic algorithms
6	Techniques, models, and algorithm types in machine learning
7	Supervised learning methods and practical examples
8	Unsupervised learning methods and clustering approaches
9	Reinforcement learning concepts and example scenarios
10	Data collection, preprocessing, and feature selection methods
11	Model training, validation, and performance evaluation metrics
12	Artificial intelligence applications in decision support systems
13	Case studies of AI and ML in various industries
14	Ethics, safety, and responsibility in artificial intelligence applications

Textbook or Material

Resources

Artificial Intelligence: A Modern Approach, Russell & Norvig

Evaluation Method and Passing Criteria

In-Term Studies	Quantity	Percentage
Attendance	-	-
Laboratory	-	-
Practice	-	-
Homework	-	-
Presentation	-	-
Projects	-	-
Quiz	-	-
Midterms	-	-
Final Exam	-	-
Total		0 (%)

ECTS / Working Load Table

	Quantity	Duration	Total Work Load
Course Week Number and Time	14	4	56
Out-of-Class Study Time (Pre-study, Library, Reinforcement)	14	4	56
Midterms	1	10	10
Quiz	0	0	0
Homework	1	10	10
Practice	0	0	0
Laboratory	0	0	0
Project	0	0	0
Workshop	0	0	0
Presentation/Seminar Preparation	1	10	10
Fieldwork	0	0	0
Final Exam	1	10	10
Other	0	0	0
Total Work Load:			152
Total Work Load / 30			5,07
Course ECTS Credits:			5

Course - Learning Outcomes Matrix

Relationship Levels
Lowest	Low	Medium	High	Highest
1	2	3	4	5

#	Learning Outcomes	P4	P5	P11
O1	Knows the basic concepts of artificial intelligence and machine learning.	5	-	-
O2	Knows machine learning modeling processes.	5	-	-
O3	Knows the data set pre-processing processes.	5	-	-
O4	Can perform data types and data type conversions.	3	-	-
O5	Explains the relationship between artificial intelligence and machine learning and other disciplines.	-	5	-
O6	Knows the usage areas of artificial intelligence and machine learning.	-	5	-
O7	Machine learning can perform model selection.	-	5	-
O8	Knows the processes of data collection and data set creation.	-	-	5