- 강의실: 공학 x관 xxx호
- Textbooks
- Kevin Patrick Murphy, Machine Learning: a Probabilistic Perspective, MIT Press, 2012 [link][pdf] [pdf2]
- Carl Edward Rasmussen and Christopher K. I. Williams, Gaussian Processes for Machine Learning, MIT Press, 2006 [link][pdf]
- D. Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press, 2012 [link][pdf]
- Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2007 [link]

- References

- Mathematics summary sheet [pdf]
- Matrix differential calculus with applications in statistics and econometrics pdf
- Python code for probabilistic machine learning [link]
- Edward: A library for probabilistic modeling, inference, and criticism [link]
- Tensorflow Distributions pdf
- Z. Ghahramani, Probabilistic machine learning and artificial intelligence, Nature '15 [pdf]

- Lecture 0: Introduction [pdf]
- Lecture 0: Introduction to machine learning [pdf] [pdf]
- Read Murphy Chap 1
- Lecture 1: Probability [pdf][pdf][pdf]
- Lecture 2: Generative models for Discrete data [pdf][pdf][pdf]
- Read Murphy Chap 3
- Ref. Generative models: Beta-Binomial, Dirichlet-Multinomial [pdf]
- Bayesian classification [pdf]
- Binomial and multinomial distributions [pdf]
- Lecture 3: Gaussian Models [pdf][pdf]
- Read Murphy Chap 4
- Bayes rules for linear Gaussian systems [pdf]
- Summary - Classification: Generative Models [pdf]
- Lecture 4: Linear regression [pdf][pdf2]
- Read Murphy Chap 7
- Ref. Linear regression [pdf]
- Lecture 5: Logistic regression [pdf][pdf2]
- Read Murphy Chap 8
- Ref. Logistic regression [pdf]
- Lecture 6: Bayesian Nonparametrics: Gaussian Processes [pdf][pdf][pdf]
- Read Murphy Chap 14-15
- Lecture 7: Bayesian neural networks [pdf]
- Read Murphy Chap 16
- Radford M. Neal, Bayesian learning for neural networks, '95 [pdf]
- D. J. Rezende, S. Mohamed, D. Wierstra, Stochastic Backpropagation and Approximate Inference in Deep Generative Models, ICML 2014 [pdf]
- Y. Gal and Z. Ghahramani, Dropout as a Bayesian approximation, ICML '16 [pdf][appendix]
- Lecture 8: Variational inference [pdf] [pdf]
- Lecture 9: Markov chain Monte Carlo (MCMC) inference [pdf] [pdf]
- Read Murphy Chap 23-24
- Lecture 10: Bayesian Nonparametrics: Dirichlet Processes
- Lecture 11: Bayesian optimization
- B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, N. de Freitas. Taking the Human Out of the Loop: A Review of Bayesian Optimization, Proceedings of the IEEE, 2016 [pdf]
- Peter I. Frazier, A Tutorial on Bayesian Optimization [pdf]
- J. Snoek, H. Larochelle, R. Adams. Practical Bayesian Optimization of Machine Learning Algorithms, NIPS '12 [pdf]
- Lecture 12: Variational Bayes

- Assignment 1: Solve problem sets
- MLAPP: Exercises 3.1, 3.13, 3.15, 3.16, 3.21

- Assignment 2: Solve problem sets
- MLAPP: Exercises 4.5, 4.7, 4.11, 4.22
- MLAPP: Exercises 7.3, 7.5, 7.9, 7.10

- Assignment 3: Solve problem sets
- MLAPP: Exercises 8.3, 8.4, 8.5, 8.7

- Assignment 4(Project): [pdf]
- Assignment 5: Solve problem sets
- MLAPP: Exercises 21.3, 21.4, 21.6
- MLAPP: Exercises 24.1, 24.2, 24.4