Generative Artificial Intelligence
ucetin@gsu.edu.tr
Content
Objective
-This course aims to examine the mathematical foundations, modern architectures, and research-level engineering approaches of generative AI systems. Students gain an in-depth understanding of how large language models (LLMs), diffusion-based image generation systems, and retrieval/agent architectures are designed, trained, optimized, and evaluated.
Course Content
(Below) It can be found in the topics section.
Course Learning Outcomes
By the end of this course, students will be able to:
- Explain the mathematical foundations underlying generative AI systems.
- Describe the architectures of large language models (LLMs), diffusion-based image generation systems, and retrieval/agent architectures.
- Design and implement generative AI models at a research level.
- Analyze and compare different architectural approaches in generative AI research.
Teaching and Learning Methods
Classes will be held in person. As part of the course, students will complete a project and present it.
References
Build a Large Language Model (From Scratch), Sebastian Raschka, September 2024
Weekly Contents
| Week | Weekly Contents |
|---|---|
| 1 | Deep Learning I |
| 2 | Deep Learning II |
| 3 | Probabilistic Language Models (Word2Vec, RNN, etc.) |
| 4 | The Mathematics of Attention |
| 5 | Deep Dive into Transformers |
| 6 | Large Language Model Training |
| 7 | Midterm Exam |
| 8 | Efficient Attention and the Long Context Problem |
| 9 | Instruction Tuning, RLHF and Alignment |
| 10 | Embedding Models and Semantic Space |
| 11 | Retrieval Augmented Generation (RAG) — Research Level |
| 12 | Agentic LLM Systems |
| 13 | Knowledge Graphs |
| 14 | Project Presentations |
Assessment System
| Activities | Number | Contribution |
|---|---|---|
| Contribution of in-term studies to overall grade | 0 | 0 |
| Contribution of final exam to overall grade | 0 | 0 |
| Total | 0 | 0 |
| Activities | Number | Contribution |
|---|---|---|
| Assignments | 0 | 0 |
| Presentation | 0 | 0 |
| Midterm Examinations (including preparation) | 1 | 30 |
| Project | 1 | 30 |
| 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 | 60 |
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 | |||||
| 12 | X | |||||
| 13 | X | |||||
ECTS
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Class Hours | 14 | 3 | 42 |
| Working Hours out of Class | 13 | 1 | 13 |
| Assignments | 0 | 0 | 0 |
| Presentation | 1 | 10 | 10 |
| Midterm Examinations (including preparation) | 1 | 15 | 15 |
| Project | 1 | 30 | 30 |
| Laboratory | 0 | 0 | 0 |
| Other Applications | 0 | 0 | 0 |
| Final Examinations (including preparation) | 1 | 15 | 15 |
| 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 | 125 | ||
| Total Workload / 25 | 5.00 | ||
| Credits ECTS | 5 | ||