ECTS - Applied Large Language Models

Atılım University - ECTS INFORMATION GUIDE

Applied Large Language Models (CMPE454) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Applied Large Language Models CMPE454 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type Elective Courses
Course Level Bachelor’s Degree (First Cycle)
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Question and Answer, Drill and Practice, Problem Solving.
Course Coordinator
Course Lecturer(s)
  • Asst. Prof. Dr. Arda Sezen
Course Assistants
Course Objectives To equip students with a solid understanding of Generative AI technologies, particularly focusing on Large Language Models (LLMs), Transformer architectures, and widely used development ecosystems such as Hugging Face. Students will explore foundational concepts, hands-on experiments, and real-world applications of LLMs.
Course Learning Outcomes The students who succeeded in this course;
  • Understand the foundations and impact of Generative AI and LLMs.
  • Gain practical experience with Hugging Face tools, NLP pipelines, and tokenization.
  • Analyze the Transformer architecture and its role in autoregressive training.
  • Examine scaling laws, pre-training approaches, and reinforcement learning with human feedback.
  • Apply parameter-efficient fine-tuning and integration of LLM components and systems.
  • Explore the use of LLM agents and compound systems in real-world tasks.
Course Content LLMs, Generative AI, tokenization, embeddings, NLP pipelines, attention, Transformer architecture, autoregressive training, LLM scaling and fine-tuning, RLHF, LLM system design, Hugging Face ecosystem, and deployment.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Course Introduction, Introduction to Generative AI Course Book – Chapter 1
2 Introduction to Hugging Face Tools and Models, Natural Language Processing and Language Models Natural Language Processing with Transformers, Revised Edition – Chapter 1
3 Tokens and Tokenization, Word Embeddings Course Book – Chapter 2
4 Experimenting with Word Embeddings, Tokens, and NLP Course Book – Chapter 2
5 Language Models and Attention Course Book – Chapter 3
6 The Transformer Architecture Course Book – Chapter 3, Build a Large Language Model – Chapter 4
7 Midterm Exam
8 Autoregressive Training Transformer LLMs Lecture Notes, Natural Language Processing with Transformers, Revised Edition – Chapter 10
9 LLM Architecture Variants, Scaling Laws in Training LLMs, Training Data for Larger LLMs Natural Language Processing with Transformers, Revised Edition – Chapter 8, Chapter 11
10 Part I: Pre-training, continued pre-training, and task training Part II: Reinforcement Learning with Human Feedback (Chat preparation) Course Book – Chapter 10, Build a Large Language Model – Chapter 5
11 Parameter-Efficient Fine-Tuning Methods Course Book – Chapter 12
12 LLM Components Lecture Notes, Course Book – Chapter 5
13 LLM Compound Systems Course Book – Chapter 7
14 LLM Agents Course Book – Chapter 7
15 Review
16 Final Exam

Sources

Course Book 1. Hands-On Large Language Models: Language Understanding and Generation, 1st Edition by Jay Alammar and Maarten Grootendorst, Publisher: O'Reilly Media, Oct. 15, 2024.
Other Sources 2. NVIDIA Deep Learning Institute: https://www.nvidia.com/en-us/training/
3. Natural Language Processing with Transformers, Revised Edition, by Lewis Tunstall, Leandro von Werra, and Thomas Wolf, Publisher: O'Reilly Media, July. 5, 2022.
4. Build a Large Language Model (From Scratch), 1st Edition by Sebastian Raschka, Publisher: Manning, Sep., 2024.
5. Hugging Face web page: https://huggingface.co/
6. PyTorch web page: https://pytorch.org/
7. TensorFlow web page: https://www.tensorflow.org/

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 1 20
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 35
Final Exam/Final Jury 1 45
Toplam 3 100
Percentage of Semester Work 55
Percentage of Final Work 45
Total 100

Course Category

Core Courses
Major Area Courses X
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Has adequate knowledge in mathematics, science, and computer engineering-specific subjects; uses theoretical and practical knowledge in these areas to solve complex engineering problems. X
2 Identifies, defines, formulates, and solves complex engineering problems; selects and applies appropriate analysis and modeling methods for this purpose. X
3 Designs a complex system, process, device, or product to meet specific requirements under realistic constraints and conditions; applies modern design methods for this purpose. X
4 Develops, selects, and uses modern techniques and tools necessary for the analysis and solution of complex problems encountered in computer engineering applications; uses information technologies effectively. X
5 Designs experiments, conducts experiments, collects data, analyzes and interprets results for the investigation of complex engineering problems or research topics specific to the discipline of computer engineering. X
6 Works effectively in disciplinary and multidisciplinary teams; gains the ability to work individually.
7 Communicates effectively in Turkish, both orally and in writing; writes effective reports and understands written reports, prepares design and production reports, makes effective presentations, gives and receives clear and understandable instructions.
8 Knows at least one foreign language; writes effective reports and understands written reports, prepares design and production reports, makes effective presentations, gives and receives clear and understandable instructions.
9 Has awareness of the necessity of lifelong learning; accesses information, follows developments in science and technology, and continuously improves oneself. X
10 Acts in accordance with ethical principles and has awareness of professional and ethical responsibility.
11 Has knowledge about the standards used in computer engineering applications. X
12 Has knowledge about workplace practices such as project management, risk management, and change management.
13 Gains awareness about entrepreneurship and innovation.
14 Has knowledge about sustainable development.
15 Has knowledge about the health, environmental, and safety impacts of computer engineering applications in universal and societal dimensions and the contemporary issues reflected in the field of engineering. X
16 Gains awareness of the legal consequences of engineering solutions.
17 Analyzes, designs, and expresses numerical computation and digital representation systems. X
18 Uses programming languages and appropriate computer engineering concepts to solve computational problems. X

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 2 32
Presentation/Seminar Prepration
Project
Report
Homework Assignments 1 18 18
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 12 12
Prepration of Final Exams/Final Jury 1 15 15
Total Workload 125