1. Courses
1.1. http://www.gatsby.ucl.ac.uk/teaching/courses/ml1-2016.html
1.2. http://www.gatsby.ucl.ac.uk/~kevinli/mlcourse/
2. References
2.1. Summary papers (Google Drive)
2.1.1. 3a) Summary by Blei and others: http://arxiv.org/abs/1601.00670v4
2.1.2. 3b) Summary by Jordan, Ghahramani and others: https://link.springer.com/article/10.1023/A:1007665907178
2.1.3. 3c) Automatic Variational Inference (this is the? foundational paper for PyMC3), this is going towards AutoDiff for VI: https://arxiv.org/abs/1301.1299
2.1.4. and http://www.jmlr.org/proceedings/papers/v33/ranganath14.pdf
2.1.5. 4a) Big paper in the latest JASA (top stats journal) about variational infrence for large-scale Bayesian regression, also goes towards modulation/automatic inference: https://www.tandfonline.com/…/full/10…/01621459.2016.1197833
2.1.6. 4b) Choice models (often used in marketing and economics and the Approximate exam 2012 :P) using VI: http://www.tandfonline.com/doi/abs/10.1198/jasa.2009.tm08030
2.1.7. 4c) Stochastic Variational Inference (with application): http://www.jmlr.org/pape…/volume14/hoffman13a/hoffman13a.pdf
2.2. Textbooks
2.2.1. Information Theory, Inference, and Learning Algorithms (MacKay) (chapter 33)
2.2.2. Machine Learning. A probablistic perpective (chapter 21)
2.2.3. Pattern Recognition & Machine Learning (Bishop) Chapter 10
2.3. Presentations
2.3.1. PyMC3 ppt
2.4. Misc (uncategorised)
2.4.1. https://link.springer.com/book/10.1007%2F978-1-4612-0745-0
2.4.2. http://mlg.eng.cam.ac.uk/yarin/thesis/thesis.pdf