Mechatronics
Course Details
KTO KARATAY UNIVERSITY
Trade and Industry Vocational School
Programme of Mechatronics
Course Details
Trade and Industry Vocational School
Programme of Mechatronics
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 03731119 | Introduction to Artificial Intelligence | 2 | Autumn | 3 | 2+0+2 | 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 | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | - |
| Instructor(s) | Lect. Taha Fatih ATEŞ |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Lect. Taha Fatih ATEŞ | T-202 | [email protected] | 7990 | Thursday 10:00 |
Course Content
Introduction to artificial intelligence, Natural and Artificial Intelligence, Turing Test, Search methods, Planning, Heuristic Problem Solving, Information representation, Predicate Logic, Artificial Intelligence Programming Languages, Programming with Common Lisp, Game Theory, Genetic Algorithms, Fuzzy Logic, Expert Systems, Statistical Methods, Artificial Intelligence Applications.
Objectives of the Course
In this course, the basic topics of artificial intelligence will be covered and the uncertain knowledge and learning components will be emphasized. Students will have the opportunity to develop a project by using one of the artificial intelligence methods in line with their interests.
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 |
|---|---|---|
| P1 | Has Fundamental, Current, And Practical Knowledge Related to Their Profession. | 3 |
| P3 | Follows and Effectively Uses Current Developments and Applications in Their Profession | 4 |
| P4 | Effectively Uses Information Technologies (Software, Programs, Animations, Etc.) Related to Their Profession | 3 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Follows industrial trends and applies them in practice | P.3.2 | 1 |
| O2 | Has knowledge about industrial standards and innovative applications | P.3.3 | 1 |
| O3 | Produces and applies creative and innovative solutions | P.3.5 | 1 |
| O4 | Effectively uses programming languages and software tools in mechatronic applications | P.4.2 | 1 |
| O5 | Uses data analysis and processing in the optimization of mechatronic systems | P.4.4 | 1 |
| O6 | Knows algorithm design and analysis techniques | P.4.8 | 1 |
| ** 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 | Introduction to Artificial Intelligence |
| 2 | Understanding the basic concepts of artificial intelligence |
| 3 | Principal Components Analysis |
| 4 | Probabilistic Sampling - Weka |
| 5 | Introduction to Artificial Neural Networks |
| 6 | Learning: Supervised and Unsupervised Learning |
| 7 | Pattern Recognition |
| 8 | Genetic Algorithms |
| 9 | Fuzzy Logic |
| 10 | Artificial Intelligence Programming Languages |
| 11 | Image and Video Processing Fundamentals |
| 12 | Image and Video Processing - Applications |
| 13 | Sound Processing |
| 14 | Human-Computer Interaction |
Textbook or Material
| Resources | Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig, Prentice Hall, 2003. |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Quiz | - | - |
| Midterms | 1 | 40 (%) |
| Final Exam | 1 | 60 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 2 | 28 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 2 | 28 |
| Midterms | 1 | 30 | 30 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 14 | 2 | 28 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 30 | 30 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 144 | ||
| Total Work Load / 30 | 4,80 | ||
| Course ECTS Credits: | 5 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P3 | P4 |
|---|---|---|---|
| O1 | Follows industrial trends and applies them in practice | 4 | - |
| O2 | Has knowledge about industrial standards and innovative applications | 4 | - |
| O3 | Produces and applies creative and innovative solutions | 4 | - |
| O4 | Effectively uses programming languages and software tools in mechatronic applications | - | 3 |
| O5 | Uses data analysis and processing in the optimization of mechatronic systems | - | 3 |
| O6 | Knows algorithm design and analysis techniques | - | 4 |