Probabilistic Graphical Models (Pengtao Xie)

Basics

• Lecture 1: Introduction to Probabilistic Graphical Models
• Lecture 2: Review of Probabilities
• Lecture 3: Review of Statistics

Beyesian Network

• Lecture 4: Introduction to Bayesian Network
• Lecture 5: Conditional Independence
• Lecture 6: Hidden Markov Models
• Lectrue 7: Autoregressive Models and ChatGPT (I)
• Lecture 8: Autoregressive Models and ChatGPT (II)

Markov Random Field

• Lecture 9: Introduction to Markov Random Field
• Lecture 10: Conditional Independence
• Lecture 11: Energy Based Models (I)
• Lecture 12: Energy Based Models (II)

Inference

• Lecture 13: Message Passing and Graph Neural Networks (I)
• Lecture 14: Message Passing and Graph Neural Networks (II)
• Lecture 15: Message Passing and Graph Neural Networks (III)
• Lecture 16: Variational Inference and Variational Autoencoder (I)
• Lecture 17: Variational Inference and Variational Autoencoder (II)
• Lecture 18: Variational Inference and Variational Autoencoder (III)
• Lecture 19: MCMC Sampling (I)
• Lecture 20: MCMC Sampling (II)
• Lecture 21: MCMC Sampling (II)

Parameter and Structure Learning

• Lecture 22: Maximum Likelihood and EM Algorithm (I)
• Lecture 23: Maximum Likelihood and EM Algorithm (II)
• Lecture 24: Maximum Likelihood and EM Algorithm (III)
• Lecture 25: Structure Learning and Neural Architecture Search (I)
• Lecture 26: Structure Learning and Neural Architecture Search (II)
• Lecture 27: Structure Learning and Neural Architecture Search (III)