Useful References

Some useful references relevant to the course. These are not required materials, they are added here for your own reference.

Papers

Paper Link
Blei, D. (2014). Build, compute, critique, repeat: Data analysis with latent variable models. Annual Review of Statistics and Its Application, 1:203–232. Link
Neal, R. M. (1993). Probabilistic inference using Markov chain Monte Carlo methods. Department of Computer Science, University of Toronto Toronto, Ontario, Canada. Link
Paisley, J., Blei, D., and Jordan, M. (2012). Variational Bayesian inference with stochastic search. Proceedings of the 29th International Conference on Machine Learning, Edinburgh, Scotland, UK. Link
Ranganath, R., Gerrish, S., and Blei, D. (2014). Black-box variational inference. In Artificial intelligence and statistics, pages 814–822. PMLR. Link
Blei, D.M., Kucukelbir, A., and McAuliffe, J.D. (2016). Variational Inference: A Review for Statisticians. In Journal of the American Statistical Association Link
Dieng, A.B., Ruiz, F.J.R., and Blei, D.M. (2019). Topic Modeling in Embedding Spaces. In Transactions of the Association for Computational Linguistics Link
Kingma, D.P., and Welling, M. (2014). Autoencoding Variational Bayes. In International Conference on Learning Representations. Link
Rezende, D.J., Mohamed, S., Wierstra, D. (2014). Stochastic backpropagation and approximate inference in deep generative models. In Proceedings of the 31st International Conference on Machine Learning (ICML), JMLR: W&CP volume 32 Link
Johnson, M.J., Duvenaud, D., Wiltschko, A.B., Datta, S.R., and Adams, R.P. (2016). Composing graphical models with neural networks for structured representations and fast inference. In 30th Conference on Neural Information Processing Systems (NeurIPS) Link
Dieng, A. B. and Paisley, J. (2020). Reweighted Expectation Maximization. Link

Books

Book Link
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). Bayesian data analysis. CRC press. Link
Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep learning, volume 1. MIT Press Cambridge. Link
Koller, D. and Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT Press. Link
Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press. Updated version website
Neal, R. M. (2012). Bayesian learning for neural networks, volume 118. Springer Science & Business Media. Link
Rasmussen, C. E. (2003). Gaussian processes in machine learning. In Summer school on machine learning, pages 63–71. Springer. Link
Robert, C. and Casella, G. (2013). Monte Carlo statistical methods. Springer Science & Business Media. Link

Lecture Notes

Notes Link
Course notes for John Paisley’s Bayesian Models for Machine Learning course at Columbia Link
Course notes for Stefano Ermon’s Probabilistic Graphical Models course at Stanford Link

Videos

These are useful videos covering some introductory material. You may go through them before taking this course (COS-513).

Video Link
Frequentism and Bayesianism: What’s the Big Deal? Link
Nando De Freitas’ Undergraduate Machine Learning course lecture videos Link