| INDUSTRY 4.0 (ENGLISH, THESIS) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| CMP5103 | Artificial Intelligence | Spring | 3 | 0 | 3 | 8 |
| Language of instruction: | English |
| Type of course: | Must Course |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Assist. Prof. TEVFİK AYTEKİN |
| Course Lecturer(s): |
Prof. Dr. NAFİZ ARICA |
| Recommended Optional Program Components: | None |
| Course Objectives: | The objective of this course is to give the student the ability to apply artificial intelligence techniques, including search heuristics, knowledge representation, planning, reasoning and learning to various problems. |
|
The students who have succeeded in this course; I. Be able to solve problems by applying a suitable search method. II. Be able to implement minimax search and alpha-beta pruning in game playing. III. Be able to use logical formalisms in modeling. IV. Be able to apply supervised learning techniques to a given problem. V. Be able to apply unsupervised learning techniques to a given problem. VI. Be able to use the basic techniques in natural language processing. |
| introduction; uninformed search strategies; informed (heuristic) search strategies; adversarial search; propositional logic; predicate logic; supervised learning techniques; unsupervised learning techniques; natural language processing. |
| Week | Subject | Related Preparation |
| 1) | Introduction | |
| 2) | Uninformed Search Strategies | |
| 3) | Uninformed Search Strategies | |
| 4) | Informed (Heuristic) Search Strategies | |
| 5) | Informed (Heuristic) Search Strategies | |
| 6) | Adversarial Search | |
| 7) | Propositional Logic | |
| 8) | Predicate logic | |
| 9) | Supervised Learning Techniques | |
| 10) | Supervised Learning Teknileri | |
| 11) | Unsupervised Learning Techniques | |
| 12) | Unsupervised Learning Techniques | |
| 13) | Natural Language Processing | |
| 14) | Natural Language Processing | |
| 15) | Final Exam |
| Course Notes / Textbooks: | Russell, S., Norvig, P., Artificial Intelligence: A Modern Approach, (3rd edition), 2009. Giarratano, J.C., Riley, G.D., Expert Systems: Principles and Programming, (4th edition), 2004. |
| References: |
| Semester Requirements | Number of Activities | Level of Contribution |
| Presentation | 1 | % 10 |
| Project | 1 | % 50 |
| Final | 1 | % 40 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 10 | |
| PERCENTAGE OF FINAL WORK | % 90 | |
| Total | % 100 | |
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 3 | 42 |
| Study Hours Out of Class | 14 | 3 | 42 |
| Project | 1 | 42 | 42 |
| Homework Assignments | 7 | 8 | 56 |
| Final | 1 | 12 | 12 |
| Total Workload | 194 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |