Akademik Dersler - Tanıtım

Last Updated: 23/06/2026

Code - Course Title	Course Type	Laboratory + Practice	ECTS
BVA219 - Large Language Models	III. SEMESTER	3 +2	6.0

Language of Instruction	Türkçe
Course Type	Required Courses
Course Instructor(s)	ÖĞR. GÖR. AHMET MÜCAHİD ARVASİ
Mode of Delivery	Face to face
Place of Delivery
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Prerequisites	There are no prerequisites or corequisites for this course.
Courses Recomended
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Work Placement
Aims of the Course

Weeks	Learning Outcomes	Topics	Teaching Methods
Week - 1		Introduction to Large Language Models: Historical Development, Transition from Statistical Language Models to LLMs, and Milestones
Week - 2		NLP Fundamentals: Word Embeddings Techniques, Limitations of RNN and LSTM Network Structures
Week - 3		Transformer Architecture 1: Attention Mechanism, Self-Attention, and Multi-Head Attention Computations
Week - 4		Transformer Architecture 2: Encoder and Decoder Structures, Positional Encoding
Week - 5		Model Training Processes 1: Masked Language Modeling (MLM), Causal Language Modeling (CLM), and Pre-training Phase
Week - 6		Model Training Processes 2: Instruction Fine-Tuning and Domain Adaptation
Week - 7		Reinforcement Learning from Human Feedback (RLHF) and Alignment Algorithms (PPO, DPO)
Week - 8		Midterm Exam
Week - 9		Prompt Engineering 1: Basic Techniques, Role Prompting, and In-Context Learning Practices
Week - 10		Prompt Engineering 2: Advanced Techniques, Chain-of-Thought, and Reasoning Templates
Week - 11		Vector Databases and RAG (Retrieval-Augmented Generation): External Information Retrieval and Document-Based LLM Integration
Week - 12		Model Optimization and Compression: Quantization Techniques (FP16, INT8, INT4) and Memory Management
Week - 13		Parameter-Efficient Fine-Tuning (PEFT): Low-Rank Adaptation (LoRA) and QLoRA Architecture
Week - 14		Ethics and Security in LLMs: Bias, Hallucination Management, Data Privacy, Safe Alignment, and End-of-Term Project Presentations

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Assessment Method and Passing Requirements
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