### 기계학습 (Machine Learning)

- 강의실: 공학 x관 xxx호
- Textbooks
- Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2007 [link] [pdf][korean]

- Textbooks (additional)
- Kevin Patrick Murphy, Machine Learning: a Probabilistic Perspective, MIT Press, 2012 [link]
- I. Goodfellow, Y. Bengio, A. Courville, Deep learning, MIT Press, 2016 [book]
- Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, MIT Press, 1998 [link] [2nd_edition]
- D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009 [book]

- References
- Matrix cookbook pdf
- Matrix differential calculus with applications in statistics and econometrics pdf
- Python notebooks for PRML text book [link]
- ML Lecture at U. Toronto [link]

- Chapter 1: Introduction [pdf] [pdf]]
- Chapter 2: Probability distributions [pdf]
- Chapter 3: Linear models for regression [pdf]
- Chapter 4: Linear models for classification [pdf][pdf][pdf][pdf][pdf]
- Tutorial: Naive Bayes and Gaussian Bayes Classier [pdf]

- Chapter 5: Neural networks [pdf][pdf]
- Chapter 6: Kernel methods
- Chapter 7: Sparse kernel machines [pdf][pdf]
- Chapter 8: Graphical models
- Chapter 9: Mixture models and EM [pdf][pdf]
- Chapter 10: Approximate inference
- Chapter 11: Sampling methods
- Chapter 12: Continuous latent variables
- Chapter 13: Sequential data
- Chapter 14: Combining models

- Assignment 1: [pdf]
- Project 1: [pdf]
- Assignment 2: [pdf]