| Course Unit Code | Course Unit Title | Type of Course Unit | Year of Study | Semester | Number of ECTS Credits | | MM233 | Artificial Intelligence Techniques in Engineering | Elective | 1 | 1 | 6 |
|
| Level of Course Unit |
| Second Cycle |
| Objectives of the Course |
| The aim of this course is to teach the students the concept of artificial intelligence techniques and their use in solving various problems in mathematics, science and engineering. |
| Name of Lecturer(s) |
|
| Learning Outcomes |
| 1 | Comprehending artificial intelligence techniques and using these techniques to solve various problems in mathematics, science and engineering | | 2 | Modeling the problems that students will face in the future and getting the necessary creativity to produce solutions. | | 3 | Increasing individual and group work and learning skills |
|
| Mode of Delivery |
| Normal Education |
| Prerequisites and co-requisities |
| None |
| Recommended Optional Programme Components |
| None |
| Course Contents |
| Neural Fuzzy Logic, Neural Fuzzy Logic, Search Algorithms and Genetic Algorithm, Genetic Algorithms, Artificial Intelligence Concept and Historical Development, Fuzzy Logic Concept, Fuzzy Relations, Sharp and Fuzzy Sets, Basic Concepts, solution space, chromosome structure, selection of fitness function. |
| Weekly Detailed Course Contents |
|
| 1 | Concept of Artificial Intelligence and Historical Development | | | | 2 | Fuzzy logic concept. | | | | 3 | Fuzzy relations, Sharp and fuzzy clusters, | | | | 4 | Establishment of rule base and Fuzzy inference | | | | 5 | Fuzzy logic based sample applications | | | | 6 | Artificial neural networks | | | | 7 | Advanced and Feedback Networks | | | | 8 | Mid-term exam | | | | 9 | Neural Fuzzy Logic | | | | 10 | Search algorithms and Genetic Algorithm | | | | 11 | Basic Concepts in Genetic Algorithms; | | | | 12 | Solution space, chromosome structure, choice of fitness function | | | | 13 | Concepts of mutation and crossover, applications in the field of mutation types, advanced topics.
" | | | | 14 | Review | | | | 15 | Case studies | | |
|
| Recommended or Required Reading |
| Peter Fish, Physics and Instrumentation of Diagnostic Medical Ultrasound
John Wiley & Sons. P.N.T. Wells, Biomedical Ultrasonics, Academic Press.
John Wiley & Sons. Joseph L. Rose and Barry B. Goldberg, Basic Physics in Diagnostic Ultrasound |
| Planned Learning Activities and Teaching Methods |
|
| Assessment Methods and Criteria | |
| Midterm Examination | 1 | 100 | | SUM | 100 | |
| Final Examination | 1 | 100 | | SUM | 100 | | Term (or Year) Learning Activities | 40 | | End Of Term (or Year) Learning Activities | 60 | | SUM | 100 |
| | Language of Instruction | | Turkish | | Work Placement(s) | | None |
|
| Workload Calculation |
|
| Midterm Examination | 1 | 1 | 1 |
| Final Examination | 1 | 2 | 2 |
| Attending Lectures | 14 | 3 | 42 |
| Practice | 1 | 12 | 12 |
| Project Preparation | 1 | 12 | 12 |
| Seminar | 1 | 6 | 6 |
| Self Study | 14 | 5 | 70 |
| Individual Study for Mid term Examination | 1 | 10 | 10 |
| Individual Study for Final Examination | 1 | 12 | 12 |
|
| Contribution of Learning Outcomes to Programme Outcomes |
|
| * Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High |
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