컴퓨터공학 특론 (Selected topics in computer science)

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
- Martin J. Wainwright1 and Michael I. Jordan, Graphical Models, Exponential Families, and Variational Inference, Foundations and Trends in Machine Learning, 2009 [pdf]
- U Toronto's ML lecture: link

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]