Software Engineering | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code: | YAZ002 | ||||
Course Name: | Introduction to Large Language Models | ||||
Semester: | Spring | ||||
Course Credits: |
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Language of instruction: | Turkish | ||||
Course Condition: | |||||
Does the Course Require Work Experience?: | Yes | ||||
Type of course: | Departmental Elective | ||||
Course Level: |
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Mode of Delivery: | Face to face | ||||
Course Coordinator: | Dr. Öğr. Üy. MUHAMMED DAVUD | ||||
Course Lecturer(s): | Dr. Öğr. Üyesi Alper Öner | ||||
Course Assistants: |
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 |
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) LLM problemlerini çözmek için yazılım araçlarını uygulayacaktır. 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. |
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 |
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 Learning Outcomes | 1 |
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Program Outcomes | |||||||||||
1) Adequate knowledge in mathematics, science and software engineering; the ability to use theoretical and practical knowledge in these areas in complex engineering problems. | 2 | 2 | 2 | 2 | 2 | ||||||
2) Ability to identify, formulate, and solve complex software engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose. | 2 | 2 | 2 | 2 | 2 | ||||||
3) Ability to design, implement, verify, validate, measure and maintain a complex software system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose. | 2 | 2 | 2 | 2 | 2 | ||||||
4) Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in software engineering applications; ability to use information technologies effectively. | 3 | 3 | 3 | 3 | 3 | ||||||
5) Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or software engineering research topics. | 2 | 2 | 2 | 2 | 2 | ||||||
6) Ability to work effectively within and multidisciplinary teams; individual study skills. | 2 | 2 | 2 | 2 | 2 | ||||||
7) Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions. | |||||||||||
8) Awareness of the necessity of lifelong learning; the ability to access information, to follow developments in science and technology and to renew continuously. | |||||||||||
9) To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in engineering applications. | |||||||||||
10) Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development. | 3 | ||||||||||
11) Knowledge of the effects of software engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in software engineering; awareness of the legal consequences of software engineering solutions. |
No Effect | 1 Lowest | 2 Average | 3 Highest |
Program Outcomes | Level of Contribution | |
1) | Adequate knowledge in mathematics, science and software engineering; the ability to use theoretical and practical knowledge in these areas in complex engineering problems. | 2 |
2) | Ability to identify, formulate, and solve complex software engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose. | 2 |
3) | Ability to design, implement, verify, validate, measure and maintain a complex software system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose. | |
4) | Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in software engineering applications; ability to use information technologies effectively. | 2 |
5) | Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or software engineering research topics. | 2 |
6) | Ability to work effectively within and multidisciplinary teams; individual study skills. | 2 |
7) | Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions. | |
8) | Awareness of the necessity of lifelong learning; the ability to access information, to follow developments in science and technology and to renew continuously. | |
9) | To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in engineering applications. | |
10) | Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development. | |
11) | Knowledge of the effects of software engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in software engineering; awareness of the legal consequences of software engineering solutions. |
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 |
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 |