| Why graphical models? | CS 228 |
| Bayes net construction | CS 228Construction |
| Bayes Net Independence | CS 228Construction |
| Bayes Net D-separation and I-maps | CS 228Construction |
| Tricks with Bayes | CS 228 |
| Markov Random Fields construction | CS 228Construction |
| Markov Independence | CS 228Construction |
| Factor graphs and conversions | CS 228Construction |
| Graphs and Bayes ⭐ | |
| Conditional Models | CS 228Inference |
| Variable Elimination | CS 228Inference |
| Message Passing | CS 228Inference |
| Junction Tree Algorithm | CS 228Inference |
| Tricks with marginals and complexity | CS 228Inference |
| Sampling Techniques | CS 228Inference |
| Rejection and Importance Sampling | CS 228Inference |
| Markov Chain Monte Carlo | CS 228Inference |
| MPE Inference | CS 228Inference |
| Fully-Observed Bayes Learning | CS 228Learning |
| MRF Learning | CS 228Learning |
| EM Algorithm | CS 228Learning |
| Bayes Learning as Inference | CS 228Learning |
| Structure Learning | CS 228Learning |
| Variational EM | CS 228Learning |
| Variational Inference (theory) | CS 228InferenceLearning |
| Variational Inference (practicals) ⭐ | CS 330 |
| Exponential Families | CS 228Learning |
| Deep Belief Networks, Boltzmann Machines | InferenceReference |