ISTINYE UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
Course Introduction and Application Information
| Course Code: | SWE015 | ||||
| Course Name: | Introduction to Large Language Models | ||||
| Semester: |
Fall |
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| Course Credits: |
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| Language of instruction: | English | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Departmental Elective | ||||
| Course Level: |
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| Mode of Delivery: | E-Learning | ||||
| Course Coordinator: | Dr. Öğr. Üy. MUHAMMED DAVUD | ||||
| Course Lecturer(s): | Assist. Prof. Dr. Alper Öner, Res. Assist. Yazım Beril Uluer | ||||
| Course Assistants: |
Course Objective and Content
| Course Objectives: | The aim of the large language model development course is to provide individuals specialized in natural language processing (NLP) with knowledge and skills in creating, training and developing large language models. This course provides in-depth knowledge of understanding complex language structures, performing language-based tasks, and using language models effectively in real-world applications. |
| Course Content: | Docker, Tensorflow, Neural Network, Convolutional Neural Network, Introduction to Neural Language Processing, Tranformers-BERT & NER, LLM, LLM Finetuning, MLLM , Computer Vision and Neural Language Processing Applications |
Learning Outcomes
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The students who have succeeded in this course;
1) Design a well-defined problem formulation for a basic LLM problem. 2) Develop optimized LLM. 3) Will apply software tools to solve LLM problems. 4) Will be able to solve Basic Image Processing and Language Problems with transformers methods. 5) Will be able to realize a Transformers Project as a team. |
Course Flow Plan
| Week | Subject | Related Preparation |
| 1) | Docker – GIT | |
| 2) | Neural Networks - Tensorflow | |
| 3) | Convolutional Neural Network | |
| 4) | Deep Learning for Computer Vision | |
| 5) | Introduction to Neural Language Processing | |
| 6) | Deep Learning for Neural Language Processing | |
| 7) | Tranformers-BERT & NER | |
| 8) | Midterm Exam | |
| 9) | Multilingual Named Entity Recognition with Transformers | |
| 10) | Making Transformers Efficient in Production | |
| 11) | Large Language Models (LLM) | |
| 12) | Open Source Large Language Models (LLM) | |
| 13) | Foundation Models | |
| 14) | LLM Applications |
Sources
| Course Notes / Textbooks: | Natural Language Processing with Transformers, Lewis Tunstall, Leandro von Werra, and Thomas Wolf, O’Reilly. |
| References: | Deep Learning with Python, François Chollet, Manning, 2018. |
Course - Program Learning Outcome Relationship
| Course Learning Outcomes | 1 |
2 |
3 |
4 |
5 |
|---|---|---|---|---|---|
| Program Outcomes |
Course - Learning Outcome Relationship
| No Effect | 1 Lowest | 2 Average | 3 Highest |
| Program Outcomes | Level of Contribution |
Assessment & Grading
| Semester Requirements | Number of Activities | Level of Contribution |
| Homework Assignments | 2 | % 20 |
| Project | 2 | % 40 |
| Final | 1 | % 40 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 60 | |
| PERCENTAGE OF FINAL WORK | % 40 | |
| total | % 100 | |
Workload and ECTS Credit Calculation
| Activities | Number of Activities | Workload |
| Course Hours | 14 | 42 |
| Presentations / Seminar | 3 | 25 |
| Project | 3 | 28 |
| Homework Assignments | 4 | 20 |
| Final | 2 | 20 |
| Total Workload | 135 | |