Adaptive methods for data-based decision making

Draft notes

notes.pdf

Schedule

  • 22-23 August: Reproducibility; kNN; Bayesian Inference (Notes: Sec 1, Sec 2.1-2.3. Additional reading: any examples from The Practice of Reproducible Research for background. )
  • 29-30 August: Decision problems; Hypothesis Testing; Neural networks; Stochastic gradient descent (Sec. 2.4 - 2.6. Additional reference material: Chapters 6-9 in DeGroot)
  • 5-6 September: Tutorial and credit risk lab
  • 12-13 September: Databases; k-anonymity; differential privacy. (Notes: Chapter 3, Additional reference material: chapters 1-3.5 in the privacy book, Additional background material: More slides on anonymity and privacy, Privacy, Big Data and the Public Good)
  • 19-20 September: Tutorial and privacy lab
  • 26-27 September: Fairness; graphical models (Notes: Chapter 4)
  • 3-4 October: Tutorial and fairness lab
  • 5 October: Project 1 deadline
  • 10-11 October: Recommendation Systems; Latent Variables; Importance Sampling (Notes: Chapter 5)
  • 17-18 October: Tutorial and recommendation lab
  • 24-25 October: Causality, interventions and counterfactuals (Notes: Chapter 6. Additional reference material: Pearl's Causality book. Additional background material: github/papers/causality/)
  • 31 Oct-1 Nov: Causality lab
  • 7-8 Nov: Bandit problems and experiment design (Notes: Chapter 7. Optionally 8.1, 8.2. Additional reference material: Chapter 6 of Dimitrakakis and Ortner.)
  • 14-15 Nov: Experiment design lab
  • 21-22 Nov: Project presentations
  • 30 Nov: Project 2 deadline