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CSCI 2470

Course Schedule

Lectures

Sep. 4, Thu
Machine Learning Recap and Perceptrons
Chen Sun
  1. Slides
  2. Recording
  3. Recommended Reading: What is AI?
Sep. 9, Tue
Loss Functions and Optimization
Chen Sun
  1. Slides
  2. Recording (Screenshare broken)
Sep. 9, Tue
HW1 Setup and Warm-up
  1. Handout
Sep. 11, Thu
Stochastic Gradient Descent and MLP
Chen Sun
  1. Slides
  2. Recording
Sep. 16, Tue
Backpropagation
Chen Sun
  1. Slides
  2. Recording
  3. Recommended Reading: Hacker’s guide to Neural Networks
Sep. 18, Thu
EXTRA Paper Nomination
  1. Form
  2. Due date: Nov. 20
Sep. 18, Thu
Deep Learning Softwares and Hardwares
Chen Sun
  1. Slides
  2. Recording
Sep. 23, Tue
Convolutional Neural Networks: Introduction
Chen Sun
  1. Slides
  2. Recording
  3. Recommended Reading: The Bitter Lesson
  4. Recommended Reading: Induction, Inductive Biases, and Infusing Knowledge into Learned Representations
Sep. 23, Tue
HW2 Convolutional Neural Networks
  1. Handout
  2. GenAI Declaration
  3. Due date: Oct. 7 6 pm ET
Sep. 25, Thu
Convolutional Neural Networks: Architectures
Chen Sun
  1. Slides
  2. Recording
Sep. 25, Thu
EXTRA Paper Presentation Recording
  1. Form
  2. Due date: Dec. 4 6 pm ET
Sep. 30, Tue
Convolutional Neural Networks in Practice
Chen Sun
  1. Slides
  2. Recording
Oct. 2, Thu
Multimodal Learning
Chen Sun
  1. Slides
  2. Recording
Oct. 2, Thu
MP1 Multimodal Learning
  1. Handout
  2. Due date: Oct. 16 6 pm ET
Oct. 7, Tue
Automatic Differentiation
Chen Sun
  1. Slides
  2. Recording
  3. Sorry the audio is not ideal, will try to understand why
Oct. 7, Tue
HW3 Beras
  1. Handout
  2. GenAI Declaration
  3. Due date: Oct. 28 6 pm ET
Oct. 9, Thu
Word Embedding and RNNs
Chen Sun
  1. Slides
  2. Recording
Oct. 14, Tue
Machine Translation
Chen Sun
  1. Slides
  2. Recording
Oct. 16, Thu
LSTM and Transformers
Chen Sun
  1. Slides
  2. Recording
Oct. 21, Tue
Transformer Applications
Chen Sun
  1. Slides
  2. Recording
Oct. 21, Tue
Final Final Project Proposal
  1. Handout
  2. Proposal Form
  3. Due date: Oct. 30 6 pm ET
Oct. 23, Thu
Graph Neural Networks, Intro to Generative Models
Chen Sun
  1. Slides
  2. Recording
Oct. 28, Tue
Deep Generative Models in Practice
Chen Sun
  1. Slides
  2. Recording
Oct. 28, Tue
Feedback Mid-term Feedback Form
  1. Form
  2. One extra late day for students who submit this form :)
Oct. 28, Tue
HW4 Image Captioning
  1. Handout
  2. GenAI Declaration
  3. Due date: Nov. 11 6 pm ET
Oct. 30, Thu
Autoencoders and Self-supervised Learning
Nate Gillman
  1. Slides
  2. Recording
Nov. 4, Tue
Variational Autoencoders
Nate Gillman
  1. Slides
  2. Recording
Nov. 6, Thu
Diffusion Models
Nate Gillman
  1. Slides
  2. Recording
Nov. 6, Thu
MP2 Diffusion and Langevin Dynamics
  1. Handout
  2. Due date: Nov. 20 6 pm ET
Nov. 11, Tue
Reinforcement Learning: Overview
Chen Sun
  1. Slides
  2. Recording
Nov. 13, Thu
INVITED A Gentle Introduction to Intelligent Agents: From Classic AI to Modern LLMs
Zi Wang
  1. Slides
  2. Recording
Nov. 18, Tue
Diffusion Models: Applications
Nate Gillman
  1. Slides
  2. Recording
Nov. 20, Thu
INVITED Invited Talk
Tianhong Li
  1. Slides
  2. Recording
Dec. 9, Tue
Final Deep Learning Day
  1. Report due date: Dec. 11 6 pm ET