Date
|
Topic
|
Lecturers
|
Materials
|
|
04/02/24
|
Intro to Large Language & Vision Models
- What is AI and why pursue it
- Weak vs Strong AI
- Intro to Large Models for vision and language
- AI4Science applications
- Current LLVMs and failures
- Class overview, homeworks, grading policy
|
|
- Slides - Part A by Pietro: pdf
- Slides - Part B by Georgia: pdf
|
|
04/04/24
|
Brief Recap on MLPs
- Definition of MLPs
- Backpropagation
- Stochastic Gradient Descent
- Momentum (paper)
- Adam (paper)
|
|
|
|
04/09/24
|
Recurrent Neural Networks
- Word Embddings
- Hidden Markov Models (HMMs)
- Sequence to Sequence (paper)
- Attention (paper)
|
|
|
|
04/11/24
|
Convolutional Neural Networks
|
|
|
|
04/16/24
|
Transformers I: Self-Attention
|
|
|
|
04/18/24
|
Guest Lecture: Towards Better Understanding of Representation
Collapsing in Representation Learning
|
|
|
|
04/23/24
|
Transformers II: Encoder
|
|
|
|
04/25/24
|
Transformers III: Decoder
|
|
|
|
04/30/24
|
Self-Supervised Learning with Large Models
|
|
|
|
05/02/24
|
Object Recognition at Scale - Part A
|
|
|
|
05/07/24
|
Object Recognition at Scale - Part B
|
|
|
|
05/09/24
|
Generative Models - Part A
|
|
|
|
05/14/24
|
Generative Models - Part B
- Variational Autoencoders
- Tutorial on VAEs (paper)
- VQ-VAE (paper)
|
|
|
|
05/16/24
|
Generative Models - Part C
- Diffusion Models
- DDPMs (paper)
- Denoising Score Matching (paper)
|
|
|
|
05/21/24
|
Generative Models - Part D
- Generative Adversarial Networks (paper)
|
|
|
|
05/23/24
|
Alignment
- Intro to RL
- REINFORCE, PPO
- InstructGPT (paper)
|
|
|
|
05/28/24
|
Responsible AI
|
|
|
|
05/30/24
|
Vision & Language
|
|
|
