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IN-STK5000
Adaptive methods for data-based decision making
Recommended literature
Optimal statistical decisions, DeGroot.
The Practice of Reproducible Research: Case Studies and Lessons from the Data-Intensive Sciences
Privacy, Big Data and the Public Good
, Julia Lane et al.
The Algorithmic Foundations of Differential Privacy
, Dwork and Roth.
Decision Making Under Uncertainty and Reinforcement Learning
, Dimitrakakis and Ortner
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Videos
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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
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