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CSCI 2470

Course Schedule

Lectures

Sep. 5, Thu
Machine Learning Recap and Perceptrons
Chen Sun
  1. Slides
  2. Recording
  3. Recommended Reading: What is AI?
Sep. 10, Tue
Loss Functions and Optimization
Chen Sun
  1. Slides
  2. Recording
Sep. 10, Tue
HW1 Math and Machine Learning Recap
  1. Handout
Sep. 12, Thu
SGD and Multi-layer Perceptron
Chen Sun
  1. Slides
  2. Recording
Sep. 17, Tue
Backpropagation
Chen Sun
  1. Slides
  2. Recording
  3. Recommended Reading: Yes you should understand backprop by Andrej Karpathy
Sep. 19, Thu
Backpropagation & TensorFlow
Chen Sun
  1. Slides
  2. Recording
  3. Demo: MNIST with MLP
Sep. 24, Tue
Convolutional Neural Networks: Introduction
Chen Sun
  1. Slides
  2. Recording
  3. Demo: MNIST with CNN
  4. Recommended Reading: Induction, Inductive Biases, and Infusing Knowledge into Learned Representations by Sam Finlayson
Sep. 24, Tue
HW2 Convolutional Neural Networks
  1. Handout
  2. Due date: Oct. 8 6 pm ET
Sep. 26, Thu
Convolutional Neural Networks: Architectures
Chen Sun
  1. Slides
  2. Recording
  3. Recommended Reading: Deep Neural Nets: 33 years ago and 33 years from now by Andrej Karpathy
Oct. 1, Tue
Interpreting Convolutional Neural Networks
Chen Sun
  1. Slides
  2. Recording
Oct. 1, Tue
MP1 Mini Project 1
  1. Handout
  2. Due date: Oct. 22 6 pm ET
Oct. 3, Thu
Convolutional Neural Networks in Practice
Chen Sun
  1. Slides
  2. Recording (1st of 2)
  3. Recording (1st of 2)
Oct. 8, Tue
AutoDiff
Chen Sun
  1. Slides
  2. Recording
Oct. 8, Tue
HW3 Beras
  1. Handout
  2. Due date: Oct. 29 6 pm ET
Oct. 10, Thu
Word Embeddings and RNNs
Calvin Luo
  1. Slides
  2. Recording
Oct. 15, Tue
Machine Translation and Attention
Chen Sun
  1. Slides
  2. Recording
Oct. 15, Tue
Final Final Project Proposal
  1. Handout
  2. Proposal Form
  3. Due date: Oct. 24 6 pm ET
Oct. 17, Thu
Transformers
Chen Sun
  1. Slides
  2. Recording
  3. Recommended Video: How Rotary Position Embedding Supercharges Modern LLMs by Jia-Bin Huang
Oct. 22, Tue
Multimodal Learning
Chen Sun
  1. Slides
  2. Recording
Oct. 24, Thu
Self-supervised Learning
Calvin Luo
  1. Slides
  2. Recording
  3. Recommended Reading: Self-supervised learning: The dark matter of intelligence
Oct. 24, Thu
MP2 Mini Project 2
  1. Handout
  2. CLIP Demo
  3. GPT Demo
  4. Due date: Nov. 12 6 pm ET
Oct. 29, Tue
Intro to Generative Models
Chen Sun
  1. Slides
  2. Recording
  3. Recommended Reading: Can Computers Create Art?
Oct. 29, Tue
HW4 Image Captioning
  1. Handout
  2. Due date: Nov. 12 6 pm ET
Oct. 31, Thu
Generative Models in Practice
Chen Sun
  1. Slides
  2. Recording
Nov. 7, Thu
Variational Autoencoders (VAE)
Calvin Luo
  1. Slides
  2. Recording
Nov. 12, Tue
Diffusion Models
Calvin Luo
  1. Slides
  2. Recording
Nov. 12, Tue
MP3 Mini Project 3
  1. Handout
  2. Jupyter Notebook
  3. Due date: Nov. 26 6 pm ET
Nov. 14, Thu
Energy Based Models
Calvin Luo
  1. Slides
  2. Recording
Nov. 19, Tue
Guest AI for Science
Dr. Fei Sha
  1. Title: Advances in Probabilistic Generative Modeling for Scientific Machine Learning
  2. Recording
Nov. 21, Thu
HW5 Reinforcement Learning
  1. Handout
  2. Due date: Dec. 5 6 pm ET
Dec. 10, Tue
Final Deep Learning Day
  1. Handout
  2. Report due date: Dec. 12 6 pm ET