| Course Type | Elective Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 5 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- Learn the concept of Artificial Intelligence, its subjects and field of interests.
- Get informed about problem solving techniques, knowledge representation and reasoning.
- Learn propositional logic and how to program using first order logic.
- Get informed about how to develop intelligent and self learning systems by understanding machine learning concept.
- Have background infromation about supervised and unsupervised classification techniques
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | Knowledge representation and reasoning. Syntax, semantics, and proof theory (deductive inference) of propositional logic. First-order predicate logic. Uncertainty. Probabilistic reasoning. Expert systems. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to Artificial Intelligence Concept. |
|
2nd Week |
Intelligent Agents. |
|
3rd Week |
Problem Solving and Searching. |
|
4th Week |
Un-informed Searching Strategies. |
|
5th Week |
Informed Searching Strategies. |
|
6th Week |
Logic Concept, Proposition and inference. |
|
7th Week |
Logic Programming: Prolog. |
|
8th Week |
Mid-term |
|
9th Week |
Problem Solving with Prolog. |
|
10th Week |
Machine Learning and General Concepts. |
|
11th Week |
Classification Methods. |
|
12th Week |
Clustering Algorithms. |
|
13th Week |
Introduction to Genetic Algorithm. |
|
14th Week |
Introduction to Artificial Neural Networks. |
|
15th Week |
General Repeat. |
|
| Recommended or Required Reading | Russell S., Norvig P. Artificial Intelligence: A Modern Approach, Prentice Hall, 2003, Second Edition, ISBN: 9780137903955.
Vasif V. Nabiyev, 2010, Yapay Zeka, Seçkin Yayıncılık, 3. Basım, Ankara.
|
|---|
| Planned Learning Activities and Teaching Methods |
- Practice
- Problem Solving
- Project
- Report Preparation
- Presentation
- Experiment
- Lecture
- Questions/Answers
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
35 |
| Assignment |
2 |
10 |
| Quiz |
4 |
15 |
| Total(%) |
|
60 |
| Contribution of In-term Studies to Overall Grade(%) |
|
60 |
| Contribution of Final Examination to Overall Grade(%) |
|
40 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
1.5 |
1.5 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
|
|
|
| Preparation for Final exam |
1 |
20 |
20 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
15 |
15 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
1.5 |
1.5 |
| Homework |
|
|
|
| Project |
1 |
70 |
70 |
| Quiz |
4 |
0.5 |
2 |
| Total Work Load | 152 |
| Total Work Load / 30 (h) | 5.06 |
| ECTS Credit of the Course | 5 |
|
| Language of Instruction | - |
|---|
| Work Placement(s) | No |
|---|
