### 확률 그래피컬 모델 (Probabilistic Graphical Models)

- 강의실: 공학 x관 xxx호
- Textbooks
- D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009 [book][pdf]
- Kevin Patrick Murphy, Machine Learning: a Probabilistic Perspective, MIT Press, 2012 [link][pdf] [pdf2]
- C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning, MIT process, 2006 [link]

- References
- Stan[link]
- Pytorch[link]
- TensorFlow [link]
- Pyro [link]
- Thoretical concept of machine leanring [pdf]
- CMU lecture on probabilistic graphical models [link]
- Stanford lecture on probabilistic graphical models [link]
- Caltech lecture on probabilistic graphical models [link]
- NYU lecture on probabilistic graphical models [link]
- UChigago lecture on probabilistic graphical models [link]
- Brown U lecture on probabilistic graphical models [link]

- Assignment 1: Solve problem sets
- MLAPP: Exercises 10.1, 10.2, 10.4

- Assignment 2: Solve problem sets
- MLAPP: Exercises 19.1, 19.5
- MLAPP: Derive Eq.(19.43) and Eq. (19.60)

- Assignment 3: [pdf]
- Assignment 4: [pdf]

- Lecture 0: Introduction [pdf]
- Lecture 1: Bayesian machine learning [pdf]
- Lecture 2: Bayesian statistics [pdf]
- Lecture 3: Frequentist statistics [pdf]
- [ref] Cramer-Rao Lower Bound and Information Geometry [pdf]

- Lecture 4: Directed models [pdf] [pdf]
- Lecture 4: Mixture models and the EM algorithm [pdf]
- Lecture 5: Undirected graphical models [pdf] [pdf]
- Lecture 6: Exact inference [pdf] [pdf]
- [ref] Junction tree algorithm [JTA I] [JTA II]
- [ref] J. S. Yedidia, W. T. Freeman, and Y. Weiss, Understanding Belief Propagation and its Generalizations [pdf]

- Lecture 7: Variational inference [pdf] [pdf]
- [ref] Variational inference: A review for statisticians [pdf]
- [ref] Latent Dirichlet Allocation (LDA) [pdf]

- Lecture 8: Markov chain Monte Carlo (MCMC) inference [pdf] [pdf]
- Lecture 9: Latent dirichlet allociation [pdf]
- [ref] Inference Methods for Latent Dirichlet Allocation [pdf]

- Lecture 10: Gaussian processes
- Lecture 11: Bayesian neural networks
- Lecture 12: Uncertainty in deep Learning