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Home > Unsupervised > ML in Health Care, Causal Inference and Motifs in Music, with Uri Shalit


	Podcast:		Unsupervised
	Episode:		ML in Health Care, Causal Inference and Motifs in Music, with Uri Shalit
	Category:		Technology
	Duration:		00:48:04
	Publish Date:		2017-12-12 12:55:00
	Description:		Uri Shalit did his Ph.d at the Hebrew University and a post doc in NYU. We talked about his research in machine learning for Health Care and what are the unique challenges in this field, about Causal Inference and how it is relevant to many machine learning problems, and about a cool study he did during his Ph.d about Motif identification in music. Relevant resources for this episode: Uri's tutorial about Causal Inference from ICML www.cs.nyu.edu/~shalit/tutorial.html An overview of a conference about Machine Learning for Health Care http://irenechen.net/blog/2017/08/22/mlhc2017.html Papers that were mentioned during the episode Causal Inference for Recommender Systems: Recommendations as Treatments: Debiasing Learning and Evaluation. You can find it here: http://www.cs.cornell.edu/~schnabts/publications.html The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables Modeling Musical Influence with Topic Models http://proceedings.mlr.press/v28/shalit13.pdf Background Material Logistic Regression from Andrew NG's course: https://www.youtube.com/watch?v=LLx4diIP83I Super short introduction to regularization: https://towardsdatascience.com/over-fitting-and-regularization-64d16100f45c Resources about L1 regularized logistic regression: http://ai.stanford.edu/~ang/papers/icml04-l1l2.pdf https://blog.alexlenail.me/what-is-the-difference-between-ridge-regression-the-lasso-and-elasticnet-ec19c71c9028 https://stats.stackexchange.com/questions/45643/why-l1-norm-for-sparse-models/159379#159379
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