| MATHEMATICS (TURKISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| GEN3002 | Artificial Intelligence | Fall | 3 | 0 | 3 | 6 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Assoc. Prof. FETHULLAH KARABİBER |
| Course Lecturer(s): |
Prof. Dr. NAFİZ ARICA Prof. Dr. SÜREYYA AKYÜZ |
| Course Objectives: | This course aims to provide: • a deep understanding of various topics in Artificial Intelligence (AI): agents, problem solving by searching, logic and reasoning, planning, probability and utility theories, learning, etc. • an introductory level understanding of AI’s application areas in bioinformatics. |
|
The students who have succeeded in this course; 1. Gains a knowledge of topics and their definitions in AI. 2. Develops an ability to design an intelligent agent from start to finish (the knowledge base, the inference mechanism, searching, handling uncertainty,...). 3. Develops an ability to program such an agent from start to finish. 4. Gains an understanding of solving problems by searching, logic and reasoning. 5. Defines application areas of AI in bioinformatics. |
| This course is an introductory level course of artificial intelligence. The course will cover the theory, and computational methods of artificial intelligence. Basic concepts include representation of knowledge and computational methods for reasoning. Applications of Artificial Intelligence to Bioinformatics will be studied. |
| Week | Subject | Related Preparation | |
| 1) | Introduction | ||
| 2) | Intelligent Agents | ||
| 3) | Solving Problems by Searching | ||
| 4) | Informed Search and Exploration | ||
| 5) | Constraint Satisfaction Problems | ||
| 6) | Adversarial Search | ||
| 7) | Logical Agents | ||
| 8) | First-Order Logic | ||
| 9) | Inference in First-Order Logic | ||
| 10) | Uncertainty | ||
| 11) | Probabilistic Reasoning | ||
| 12) | Making Simple Decisions | ||
| 13) | Learning from Observations | ||
| 14) | Applications of AI in Bioinformatics | ||
| Course Notes: | Course notes will be given weekly. |
| References: | 1. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall; 3rd edition, 2009. |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | % 0 | |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | % 0 | |
| Homework Assignments | 2 | % 10 |
| Presentation | % 0 | |
| Project | 1 | % 25 |
| Seminar | % 0 | |
| Midterms | 1 | % 25 |
| Preliminary Jury | % 0 | |
| Final | 1 | % 40 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 35 | |
| PERCENTAGE OF FINAL WORK | % 65 | |
| Total | % 100 | |
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Special Course Internship (Work Placement) | 0 | 0 | 0 |
| Field Work | 0 | 0 | 0 |
| Study Hours Out of Class | 14 | 7 | 98 |
| Presentations / Seminar | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework Assignments | 0 | 0 | 0 |
| Quizzes | 0 | 0 | 0 |
| Preliminary Jury | 0 | ||
| Midterms | 1 | 2 | 2 |
| Paper Submission | 0 | ||
| Jury | 0 | ||
| Final | 1 | 2 | 2 |
| Total Workload | 144 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |