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DS-GA 3001: Tools and Techniques for Machine Learning (NYU Fall 2021)

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Course overview

  • DS-GA 3001: Tools and Techniques for Machine Learning
  • Instructor: David Rosenberg
  • Term: Fall 2021

This course deals with a range of topics that come up when applying machine learning in practice. Much of the course builds towards an understanding of how to handle machine learning with interventions, in contexts such as counterfactual learning, reinforcement learning, and causal inference. Techniques for inverse propensity weighting and for attaining double robustness with control variates will be given special attention, as these have applications throughout this course and beyond. Along the way, we’ll discuss covariate shift, the exploration/exploitation tradeoff, and supervised learning with black box (including non-differentiable) loss functions. Finally, we will cover probability forecast calibration and methods for interpreting machine learning models.

Prerequisites

Topics and rough schedule

The following is a tentative schedule for the semester:

  • Week 1: Conditional expectation and variance decomposition
  • Week 2: Response bias, inverse propensity weighting, and self-normalization
  • Week 3: Regression imputation, covariate shift
  • Week 4: Control variates, double robustness, average treatment effects
  • Week 5: Conditional average treatment effects (CATE), bandits, Thompson sampling
  • Week 6: Contextual bandits, counterfactual evaluation
  • Week 7: Counterfactual learning
  • Week 8: Policy gradient for bandits and contextual bandits
  • Week 9: Variance reduction for policy gradient, supervised learning with black box losses
  • Week 10: Reinforcement learning, proper scoring rules
  • Week 11: Calibrated probabilities
  • Week 12: Feature importance and global interpretability
  • Week 13: Local interpretation, LIME, Shapley values
  • Week 14: SHAP

More information and motivation for these topics can be found an earlier draft of the syllabus here.

  • Fall 2021 Syllabus
  • This file shows the lecture dependencies and is clickable to get to the slides described in each node

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DS-GA 3001: Tools and Techniques for Machine Learning (NYU Fall 2021)

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