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
|
|
|
|
04/16/23
|
Transformers I: Self-Attention
|
|
|
|
04/18/23
|
Guest Lecture: Towards Better Understanding of Representation
Collapsing in Representation Learning
|
|
|
