Machine Learning Advanced projects/labs.
Arguments:
Module 1 - Bayesian inference: Bishop chapter 1: Entire chapter Bishop chapter 2: 2.1, 2.2 and 2.3. Read the rest of the chapter as background.
Module 2 - Directed Graphical Models: Bishop chapter 8: 8.1 - 8.4.3 Murphy chapter 4.1 and 4.2
Module 3 - Variational Inference: Bishop chapter 10: 10.1 - 10.3.1 (except 10.2.2 and 10.2.3) Murphy chapter 10.3.1
Module 4 - Stochastic Variational Inference Original paper Links to an external site. Murphy chapter 10.1.4
Module 5 - Black-Box Variational Inference Original paper Links to an external site. Murphy chapter 10.2 intro and 10.2.3
Module 6 - Variational Autoencoders: Kingma, Diederik P., and Max Welling. "Auto-encoding variational bayes." arXiv preprint arXiv:1312.6114 Links to an external site. (2013) Links to an external site.. Murphy chapter 10.2.1, 21.1, 21.2.1, 21.2.2 and 21.2.3
Module 7: Chapters 1, 2 and 4 from Lee, John A., and Michel Verleysen. Nonlinear dimensionality reduction. Springer Science & Business Media, 2007. Chapter 12 (optional) from Bishop, Christopher M. Pattern Recognition and Machine Learning. New York :Springer, 2006.
Module 8: Chapters 3 and 4 (reference for those not familiar with the basic probability discussed in videos) from Motwani, Rajeev, and Prabhakar Raghavan. Randomized algorithms. Cambridge university press, 1995. Dasgupta, Sanjoy, and Anupam Gupta. "An elementary proof of a theorem of Johnson and Lindenstrauss." Random Structures & Algorithms 22.1 (2003): 60-65. or Achlioptas, Dimitris. "Database-friendly random projections." Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. 2001.
Module 9: Von Luxburg, Ulrike. "A tutorial on spectral clustering." Statistics and computing 17.4 (2007): 395-416. Chapters 1, 2, 3 and 5 from Hamilton, William L. "Graph representation learning." Synthesis Lectures on Artifical Intelligence and Machine Learning 14.3 (2020): 1-159.