Course Schedule

Weekwise Schedule

Tentative and subject to change

Week	Module	Topic Code	Topic
1	LLM Foundations I	1.1	Orientation, Transformer Architecture
		1.2	GPT-2
2	LLM Foundations II	2.1	Modern Architectures
		2.2	Mixture of Experts
3	GPU Basics	3.1	GPU Architecture Deep Dive
		3.2	Parallelism: Multi GPU, Multi Node
4	Inference	4.1	Inference Strategies
		4.2	Inference Math and Bottlenecks
5	Efficient Inference & Quantization	5.1	Efficient Attention & KV Caching
		5.2	Quantization Fundamentals
6	Inference	6.1	Inference Engines and Multi GPU
7	Fine-Tuning Fundamentals	7.1	RAG Fundamentals – Context Engineering, Embeddings, Search and Rerankers
8	RAG	8.1	Evaluating RAG
		8.2	ReAct Framework: Thought → Action → Observation
9	Reasoning	9.1	Tool Calling, Agents
	RAG	9.2	Fine Tuning for Tool Calling
10	Instruction Tuning	10.1	Instruction Tuning
	Agents	10.2	Alignment (RLHF, DPO etc)
11		11.1	More RL
		11.2	Reasoning & Chain-of-Thought
12	Evaluation	12.1	Evaluation I
		12.2	Evaluation II

Materials

• Lecture slides and notes will be shared here as class progresses.