The following syllabus is under construction and subject to change until January 2026.
Course Description
Large language models are a foundational building block of generative AI systems. Modern LLMs go far beyond the capabilities of early LLMs like GPT-3 by demonstrating the capacity for reasoning in natural language. This course provides a hands-on crash course into what makes these models work, covering:
- Fundamental building blocks like tokenizers and optimizers
- Optimizations such as FlashAttention
- Approaches for learning reasoners such as supervised fine-tuning, reinforcement learning, and inference-time methods
Assignments will focus on implementation of these approaches. We will start with a moderately intensive programming assignment requiring implementing a Transformer language model from scratch and making it fast. You will be expected to do this in the first few weeks of the semester.
Recommended Prerequisites
- CSCI-GA.2590 Natural Language Processing or equivalent. You should be familiar with the Transformer architecture (fairly comfortable with the material in this post).
- CSCI-GA.2565 Machine Learning or equivalent
- Deep learning or equivalent
- Python programming experience, including PyTorch
- Experience with concepts from probability and linear algebra
Grading & Components
- 25% Assignments (3 assignments)
- 25% Homework Quizzes (3–4 quizzes)
- 25% Midterm Exam
- 25% Final Project
Assignments
Tentatively there are slated to be three programming assignments. We will follow up about GPU resources closer to the start of the semester.
Schedule (Tentative)
Topics, dates, and assignments are approximate and subject to change. There will be a midterm exam, to be scheduled.
| Date | Topic | Details | Assignment |
|---|---|---|---|
| Jan 23 | Intro, Transformers Review | Course overview; expectations; review of the Transformer architecture. | Assignment 1 released |
| Jan 30 | Tokenizers, Optimizers, and Tricks | Tokenizers, positional encodings, optimizers, and other stuff needed to make Transformer LLMs work. | — |
| Feb 6 | Making LLMs Fast I | GPUs, memory layouts, and FlashAttention. | — |
| Feb 13 | Making LLMs Fast II | Inference-time optimization, KV caching | Assignment 1 due · Assignment 2 released |
| Feb 20 | Scaling Laws | Empirical scaling laws with connections to convergence rates of estimators, model/data/compute tradeoffs. | — |
| Feb 27 | Training I: SFT & RLHF | Supervised fine-tuning, reinforcement learning from human feedback, and how they're used to train LLMs | — |
| Mar 6 | Training II: GRPO & RLVR | Training methods for modern reasoners | Assignment 2 due · Assignment 3 released |
| Mar 13 | TBD | Topic to be announced. | — |
| Mar 20 | No Class | Spring Break. | — |
| Mar 27 | TBD | Topic to be announced. | Assignment 3 due |
| Apr 3 | LLM Evaluation | Principles for evaluating LLMs, including statistical testing and benchmark design practices | — |
| Apr 10 | Multimodality | LLMs beyond text: vision, audio, and other modalities. | — |
| Apr 17 | Agents | Tool calling, MCP, computer use agents, and agentic workflows. | — |
| Apr 24 | TBD | Topic to be announced. | — |
| May 1 | Project Presentations | Final project presentations and wrap-up. | Final project due date TBD around this time |
Other Policies
A complete syllabus including policies on accommodations, late assignments, academic integrity, and other course logistics will be posted closer to the date of the course.