COMPUTER ENGINEERING (ENGLISH, PHD) | |||||
PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
CMP5103 | Artificial Intelligence | Fall Spring |
3 | 0 | 3 | 8 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Language of instruction: | English |
Type of course: | Departmental Elective |
Course Level: | |
Mode of Delivery: | Face to face |
Course Coordinator : | Dr. Öğr. Üyesi 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 | |
1) | Ability to identify and apply advanced concepts in computer engineering | 4 |
2) | Cooperate efficiently in intra-disciplinary and multi-disciplinary teams. | 4 |
3) | Have theoretical and practical basis in computer engineering and science to be able to conduct related academic research independently. | 4 |
4) | Ability to apply advanced mathematical and engineering knowledge on real problems. | 4 |
5) | Ability to search the scientific literature related to a research project and find the relationships with own research | 4 |
6) | Ability to interprete scientific research and use the findings | 4 |
7) | Ability to work efficiently in interdisciplinary research teams | 5 |
8) | Ability to attain scientific knowledge | 5 |
9) | Ability find ways to improve upon current knowledge | 4 |
10) | Ability to present research findings in seminars and conferences | 4 |
11) | Ability to write research progress reports by referring to published theses and papers. | 4 |
12) | Ability to show the responsibility of professional and ethical behavior | 5 |