(INF 537)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| INF 537 | 2 | 3 | 0 | 0 | 3 | 6 |
| Prerequisites | |
| Admission Requirements |
| Language of Instruction | English |
| Course Type | Elective |
| Course Level | Masters Degree |
| Course Instructor(s) | Uzay ÇETİN ucetin@gsu.edu.tr (Email) |
| Assistant | |
| 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. |
| 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 |
Theory Topics
| 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 |
Practice Topics
| Week | Weekly Contents |
|---|
Contribution to Overall Grade
| Number | Contribution | |
|---|---|---|
| Contribution of in-term studies to overall grade | 0 | 0 |
| Contribution of final exam to overall grade | 0 | 0 |
| Toplam | 0 | 0 |
In-Term Studies
| 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 |
| Toplam | 2 | 60 |
| 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 | |||||
| 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 | ||