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
Tutorial 1:
Broadcasting + Matrix Calculus
Tutorial 2:
Backprop + Vectorization
4
Wed, January 28
Lecture 5:
Convolutional Neural Networks (CNNs)
Lecture 6:
Activation Functions & Normalization Layers
5
Wed, February 4
Lecture 7:
Optimizers
Lecture 8:
Interpretability
6
Wed, February 11
Lecture 9:
Vision and Language Tokenization
7
Wed, February 18
Quiz 1
Lecture 11:
Attention and Transformers
8
Wed, February 25
Lecture 12:
Modern Architectures
Lecture 13:
Structured Prediction
9
Wed, March 4
Lecture 14:
Self-Supervised Learning
Lecture 15:
Foundation Models - Language
Lecture 16:
Foundation Models - Multimodal
Fri, March 20
A5 Due (Extra Credit)