Search

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: 00:48:05
Publish Date: 2017-12-12 18:55:00
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
Total Play: 0