Generative AI and Foundation Models, Spring 2024

This is the public course website for Generative AI and Foundation Models, Spring 2024.


  • This course meets once a week, and I will talk about the recent advancements in generative AI (image and text) that shook up the world.
  • First lecture will be on Friday 03.08 9:00–11:59am, 43-101.
  • We will have a make-up lecture on Saturday 03.09 9:00–11:59am, 28-101.


Lecture Topics

Continuous-depth neural networks

  • Neural ODE
  • Continuous depth flow models

Diffusion models:

  • Stochastic differential equations and Itô calculus
  • Fokker–Planck equation and reverse-time diffusion
  • Diffusion generative models via stochastic differential equations
  • Score matching and discrete-time diffusion models
  • Conditional generation
  • Latent diffusion model
  • DALLE 2, Imagen, Stable Diffusion
  • Consistency trajectory models
  • Note: Mathematical rigor will not be a priority for this course. For diffusion models, however, we will carry out calculations and derivations with stochastic differential equations.

Natural language processing background:

  • Sequence models and text preprocessing
  • Recurrent neural networks, GRU, and LSTM
  • Bidirectional RNN
  • Encoder-decoder architecture and machine translation
  • Bahdanau attention
  • Multi-head attention and transformers

Large language models:

  • Instruction finetuning
  • Reinforcement leaning with human feedback
  • BERT, T5, GPT
  • Scaling laws
  • In-context learning
  • Chain of thought prompting
  • Codex
  • Parameter-efficient fine tuning (LoRA)
  • Hardware-aware models: FlashAttention, QLoRA
  • Training data curation and small language models: phi-X

Self-supervised learning:

Vision language models:

  • Vision transformer
  • CLIP
  • BLiP, Flamingo, LLaVA

State-space models:

  • Background: orthogonal polynomials, matrix exponential, linear state-space models
  • Continuous-time recurrent memory unit
  • Background: fast Fourier transform, Woodbury matrix identity
  • Structured state-space model
  • Background: prefix sum, kernel fusion
  • Mamba

Course Information

Course material will be posted on this website. eTL will be used for announcements, homework submission, and receiving homework and exam scores.


Ernest K. Ryu, 27-205,

Photo of Ernest Ryu


Fridays 9:00–11:59am, 43-101.


Attendance 30%, homework 30%, final exam 40%.


Good knowledge of the following subjects is required.

  • Basics of deep neural network architecturs at the level of ResNet.
  • Basics of deep neural network training at the level of SGD, Adam, and BatchNorm.
  • Basic ODEs: Initial value problem.
  • Probability theory at the level of conditional expectations and multi-variate Gaussians. Prior exposure to SDEs is not a prerequisite.