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MACHINE LEARNING
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9 resources-
Winn, J. M. (2019). Model-Based Machine Learning. Taylor & Francis Incorporated.
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Jacobs, B. (2018). Categorical Aspects of Parameter Learning. ArXiv:1810.05814 [Cs]. Retrieved from http://arxiv.org/abs/1810.05814
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Tran, D., Hoffman, M. D., Saurous, R. A., Brevdo, E., Murphy, K., & Blei, D. M. (2017). Deep Probabilistic Programming. ArXiv:1701.03757 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1701.03757
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Jacobs, B., & Zanasi, F. (2016). A Predicate/State Transformer Semantics for Bayesian Learning. Electronic Notes in Theoretical Computer Science, 325, 185–200. https://doi.org/10/ggdgbb
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Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452–459. https://doi.org/10/gdxwhq
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Culbertson, J., & Sturtz, K. (2013). Bayesian machine learning via category theory. ArXiv:1312.1445 [Math]. Retrieved from http://arxiv.org/abs/1312.1445
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Watanabe, S. (2009, August). Algebraic Geometry and Statistical Learning Theory. https://doi.org/10.1017/CBO9780511800474
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McCullagh, P. (2002). What is a statistical model? The Annals of Statistics, 30(5), 1225–1310. https://doi.org/10/bkts3m
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Wermuth, N., & Cox, D. R. (2001). Graphical Models: Overview. In N. J. Smelser & P. B. Baltes (Eds.), International Encyclopedia of the Social & Behavioral Sciences (pp. 6379–6386). Oxford: Pergamon. https://doi.org/10.1016/B0-08-043076-7/00440-X
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