• Intro to ML
  • Probability
  • Linear Algebra
  • Neural Networks
  • Sequence Models
  • Deep Learning for vision
  • Recommender Systems
  • Generative Models
  • Reinforcement Learning
  • Graphs and ML
  • Matching Networks
  • Monte Carlo Methods
  • Advanced ( stable diffusion, Flamingo, multimodal, joint learning)