Deep Generative Models (Pengtao Xie)

Graphical Models

• Lecture 1: Introduction and Bayesian Network
• Lecture 2: Markov Random Field

Inference

• Lecture 3: Variational Inference and Variational Autoencoder (I)
• Lecture 4: Variational Inference and Variational Autoencoder (II)
• Lecture 5: MCMC Sampling (I)
• Lecture 6: MCMC Sampling (II)

Learning

• Lecture 7: Maximum Likelihood and EM Algorithm

Deep Generative Models

• Lecture 8: Generative Adversarial Networks

• Lecture 9: Basics of Large Language Models (LLMs)
• Lecture 11: Multi-modal LLMs
• Lecture 13: Parameter-Efficient Finetuning of LLMs
• Lecture 10: LLM Reasoning

• Lecture 12: LLM Watermarking

• Lecture 14: Basics of Diffusion Models
• Lecture 15: Advanced Topics of Diffusion Models (I)
• Lecture 16: Advanced Topics of Diffusion Models (II)

Applications and Course Project Presentations

• Lecture 17: LLMs for Biomedicine
• Lecture 18: Diffusion Models for World Modeling
• Lecture 19: Course Project Presentation (I)
• Lecture 20: Course Project Presentation (II)