Artificial Intelligence and Deep Learning
tnaskali@gsu.edu.tr
Content
Objective
This course aims to provide students with a comprehensive understanding of artificial intelligence and deep learning processes. Students will learn to understand machine learning and AI workflows, the importance of data, and how to translate objectives into system hyperparameters and inputs.
Course Content
The course begins with fundamental deep learning concepts, focusing on CNN and RNN architectures. It then covers reinforcement learning, genetic algorithms, Deep Q-Learning, and NEAT algorithms. Emphasis is placed on understanding AI applications, the importance of data, and hyperparameter optimization throughout the course.
Course Learning Outcomes
- Explain deep learning and AI processes
- Understand CNN and RNN architectures
- Understand the fundamentals of reinforcement learning and genetic algorithms
- Implement Deep Q-Learning and NEAT algorithms
- Analyze data effectively and determine model hyperparameters
Teaching and Learning Methods
- Lectures supported by visual examples and real-world applications
- Conceptual discussions and Q&A sessions
- Coding exercises and problem-solving sessions
- Mini projects and algorithm implementations
References
- Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction
- Stanley, K., & Miikkulainen, R. (2002). NEAT: NeuroEvolution of Augmenting Topologies
- Online tutorials, research papers, and Python libraries such as PyTorch and TensorFlow
Weekly Contents
| Week | Weekly Contents |
|---|---|
| 1 | Introduction to AI and machine learning |
| 2 | Importance of data and preprocessing |
| 3 | Fundamental deep learning concepts |
| 4 | Convolutional Neural Networks (CNN) |
| 5 | CNN applications and advanced techniques |
| 6 | Recurrent Neural Networks (RNN) |
| 7 | RNN applications and optimization techniques |
| 8 | Introduction to reinforcement learning |
| 9 | Deep Q-Learning |
| 10 | Genetic algorithms |
| 11 | Project presentations and evaluation |
Assessment System
| Activities | Number | Contribution |
|---|---|---|
| Contribution of in-term studies to overall grade | 2 | 50 |
| Contribution of final exam to overall grade | 1 | 50 |
| Total | 3 | 100 |
| Activities | Number | Contribution |
|---|---|---|
| Assignments | 0 | 0 |
| Presentation | 1 | 25 |
| Midterm Examinations (including preparation) | 0 | 0 |
| Project | 1 | 25 |
| Laboratory | 0 | 0 |
| Other Applications | 0 | 0 |
| Quiz | 0 | 0 |
| Term Paper/ Project | 0 | 0 |
| Portfolio Study | 0 | 0 |
| Reports | 0 | 0 |
| Learning Diary | 0 | 0 |
| Thesis/ Project | 0 | 0 |
| Seminar | 0 | 0 |
| Other | 0 | 0 |
| Make-up | 0 | 0 |
| Total | 2 | 50 |
Relation of Proficiency
| No | Program Learning Outcomes | Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | X | |||||
| 2 | X | |||||
| 3 | X | |||||
| 4 | X | |||||
| 5 | X | |||||
| 6 | X | |||||
| 7 | X | |||||
| 8 | X | |||||
| 9 | X | |||||
| 10 | X | |||||
| 11 | X | |||||
ECTS
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Class Hours | 11 | 4 | 44 |
| Working Hours out of Class | 11 | 9 | 99 |
| Assignments | 0 | 0 | 0 |
| Presentation | 1 | 18 | 18 |
| Midterm Examinations (including preparation) | 0 | 0 | 0 |
| Project | 1 | 17 | 17 |
| Laboratory | 0 | 0 | 0 |
| Other Applications | 0 | 0 | 0 |
| Final Examinations (including preparation) | 1 | 22 | 22 |
| Quiz | 0 | 0 | 0 |
| Term Paper/ Project | 0 | 0 | 0 |
| Portfolio Study | 0 | 0 | 0 |
| Reports | 0 | 0 | 0 |
| Learning Diary | 0 | 0 | 0 |
| Thesis/ Project | 0 | 0 | 0 |
| Seminar | 0 | 0 | 0 |
| Other | 0 | 0 | 0 |
| Make-up | 0 | 0 | 0 |
| Yıl Sonu | 0 | 0 | 0 |
| Hazırlık Yıl Sonu | 0 | 0 | 0 |
| Hazırlık Bütünleme | 0 | 0 | 0 |
| Total Workload | 200 | ||
| Total Workload / 25 | 8.00 | ||
| Credits ECTS | 8 | ||