| 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;
- Be informed of knowledge representation and reasoning.
- Know syntax, semantics, and proof theory (deductive inference) of propositional logic.
- Develop probabilistic reasoning applications.
- Knows uncertainty and self-learning.
- Recognize expert systems.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | Semantic nets and description matching. Generate and test, Means-ends analysis, and problem reduction. Nets and basic search. Nets and optimal search. Trees and adversarial search. Rules and rule chaining. Rules, substrates, and cognitive modeling. Frames and inheritance. Frames and commonsense. Numeric constraints and propagation. Symbolic constraints and propagation. Logic and resolution proof. Backtracking and truth maintenance. Planning. Learning by analyzing differences. Learning by explaining experience. Learning by correcting mistakes. Learning by recording cases. Learning by managing multiple models. Learning by building identification trees. Learning by training neural nets. Learning by training perceptrons. Learning by training approximation nets. Learning by simulating evolution. Recognizing objects. Describing images. Expressing language constraints. Responding to questions and commands. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to artificial intelligence, basic terms, current models of artificial intelligence |
|
2nd Week |
Artificial Neural Networks |
|
3rd Week |
Artificial Neural Networks |
|
4th Week |
Artificial Neural Networks |
|
5th Week |
Artificial Neural Networks |
|
6th Week |
Machine Learning |
|
7th Week |
Machine Learning |
|
8th Week |
MIDTERM EXAM |
|
9th Week |
Machine Learning |
|
10th Week |
Recurrent Neural Networks |
|
11th Week |
Long Short-Term Memory Networks |
|
12th Week |
Convolutional Neural Networks |
|
13th Week |
Fuzzy Logic |
|
14th Week |
Genetic Algorithm |
|
15th Week |
|
|
| Recommended or Required Reading | 1)Artificial Intelligence, Patrick Henry Winston, 1992/Third Edition, Addison-Wesley..
2)Artificial Intelligence: Foundations of Computational Agents, David L. Poole and Alan K. Mackworth, 2017/Second Edition, Cambridge University Press
3)The Quest for Artificial Intelligence: A History of Ideas and Achievements, Nils Nilsson, 2009, Cambridge University Press.. |
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Questions/Answers
- Presentation
- Practice
- Problem Solving
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
25 |
| Project |
1 |
20 |
| Presentation of Article |
1 |
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 |
2 |
2 |
| Preparation for Quiz |
4 |
10 |
40 |
| Individual or group work |
1 |
15 |
15 |
| Preparation for Final exam |
10 |
3 |
30 |
| Course hours |
1 |
10 |
10 |
| Preparation for Midterm exam |
1 |
10 |
10 |
| Laboratory (including preparation) |
0 |
0 |
0 |
| Final exam |
1 |
2 |
2 |
| Homework |
2 |
20 |
40 |
| Total Work Load | 149 |
| Total Work Load / 30 (h) | 4.96 |
| ECTS Credit of the Course | 5 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
