1
Wed, January 7
Lecture 1:
Introduction
Lecture 2:
Image Classification with Linear Classifiers
2
Wed, January 14
Lecture 3:
Regularization and Optimization
Lecture 4:
Neural Networks and Backpropagation
3
Wed, January 21
Lecture 5:
Tutorial: Broadcasting + Matrix Calculus
Lecture 6:
Tutorial: Backprop + Vectorization
4
Wed, January 28
Lecture 7:
Convolutional Neural Networks (CNNs)
Lecture 8:
Activation Functions & Normalization Layers
5
Wed, February 4
Lecture 9:
Optimizers
Lecture 10:
Interpretability
6
Wed, February 11
Lecture 11:
Vision and Language Tokenization
7
Wed, February 18
Midterm 1
Lecture 13:
Attention and Transformers
8
Wed, February 25
Lecture 14:
Modern Architectures
Lecture 15:
Structured Prediction
9
Wed, March 4
Lecture 16:
Self-Supervised Learning
Lecture 17:
Foundation Models - Language
10
Wed, March 11
Midterm 2
Lecture 18:
Foundation Models - Multimodal