| Course Type | Elective Course |
|---|
| Course Level | Associate Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 3 |
|---|
| Name of Lecturer(s) |
- |
| Learning Outcomes |
At the end of this course, the students;
- Explains the fundamental concepts, history, and development of artificial intelligence.
- Compares supervised, unsupervised, and reinforcement learning methods and applies them to appropriate scenarios.
- Describes the basic structures of artificial neural networks and deep learning models and explains their working principles.
- Explains and evaluates the core principles of advanced techniques such as natural language processing and generative AI (GANs) with examples.
- Analyzes the applications of artificial intelligence in various engineering domains (e.g., medicine, finance, games).
- Evaluates the ethical, societal, and legal impacts of artificial intelligence systems.
- Interprets future trends and emerging technologies in artificial intelligence.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | |
|---|
| Course Description | History of Artificial Intelligence. Metaheuristic Optimization. Machine Learning. Unsupervised Learning. Reinforcement Learning. Supervised Learning. Big Data, Computation Technology, Deep Learning, Convolutional Neural Networks, Computer Vision. Generative Adverserial Networks. AI in Medicine.Ethics in AI. Time Series Prediction, Recurrent Neural Networks. Natural Language Processing. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Artificial Intelligence History. |
|
2nd Week |
Metaheuristic Optimization. |
|
3rd Week |
Machine Learning. |
|
4th Week |
Unsupervised Learning. |
|
5th Week |
Reinforcement Learning. |
|
6th Week |
Supervised Learning. |
|
7th Week |
Big Data, Computing Technology, |
|
8th Week |
Midterm |
|
9th Week |
Deep Learning, Convolutional Neural Networks, Computer Vision. |
|
10th Week |
Generative Adverserial Networks and ethics in applications. |
|
11th Week |
Artificial Intelligence in Medicine and ethics in applications. |
|
12th Week |
Time Series Prediction, Iterative Neural Networks. |
|
13th Week |
Natural Language Processing. |
|
14th Week |
The Future of Artificial Intelligence |
|
| Recommended or Required Reading | |
|---|
| Planned Learning Activities and Teaching Methods |
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Assignment |
2 |
15 |
| Attendance |
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 |
1.5 |
1.5 |
| Preparation for Quiz |
0 |
0 |
0 |
| Individual or group work |
14 |
2.5 |
35 |
| Preparation for Final exam |
1 |
10 |
10 |
| Course hours |
14 |
2 |
28 |
| Preparation for Midterm exam |
1 |
7 |
7 |
| Laboratory (including preparation) |
0 |
0 |
0 |
| Final exam |
1 |
1.5 |
1.5 |
| Homework |
2 |
3.5 |
7 |
| Total Work Load | 90 |
| Total Work Load / 30 (h) | 3 |
| ECTS Credit of the Course | 3 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
