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Deep Learning Book: http://www.deeplearningbook.org
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Tutorials and introductory documents: http://www.1-4-5.net/~dmm/ml
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Bayesian Inference: http://www.1-4-5.net/~dmm/ml/ps.pdf
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Variational Inference: http://www.1-4-5.net/~dmm/ml/vae.pdf (it is a form of approximate inference that, among other things, attempts to deal with the intractable integral in Bayes' Rule, the "evidence" aka "marginal likelihood").
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Deep Learning papers: https://github.com/terryum/awesome-deep-learning-papers
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Andrew Ng's Coursera ML course, Logistic Regression: https://www.youtube.com/watch?v=LLx4diIP83I
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Various: http://www.1-4-5.net/~dmm/vita.html (under Recent Talks) and https://github.com/davidmeyer/ml