Syllabus
This syllabus is under development and is subject to change. Unless otherwise noted, recorded videos for discussions will be released Tuesdays and Wednesday discussion hours will be reserved for questions about the discussion material. Labs, discussions, and HW releases/deadlines will occur on the Monday, Wednesday, and Fridays following the dates below, respectively.
If you are requesting an extension for a HW or lab assignment (due to DSP accommodations), please email any of the GSIs prior to the original deadline date. Please avoid emailing the professors.
| Week | Lecture | Date | Topic | Lab | Discussion | Homework |
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| 1 | 0 | Tuesday 08/25/20 |
(No class)
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| 1 | Thursday 8/27/20 |
Course Overview, Making Decisions Under Uncertainty [slides]We provide an overview of data science and where it might head in the future. We discuss the importance of decision-making rather than simple inference. As a first step in this direction we cover the different angles through which a simple hypothesis test can be viewed.
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| 2 | 2 | Tuesday 9/1/20 |
Multiple Hypothesis Testing [slides]We continue investigating the different ways of understanding error in hypothesis testing. We introduce the Neyman-Pearson principle and Neyman-Pearson Lemma for controlling error in a single test, and also introduce the challenges involved in multiple hypothesis testing.
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| 3 | Thursday 9/3/20 |
False Discovery Rate Control [slides]We consider the Bonferroni test, which is a naive way of controlling the family-wise error rate. We then look into Benjamini-Hochberg which is a more elaborate method that controls the false discovery rate.
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| 3 | 4 | Tuesday 9/8/20 |
Online False Discovery Rate Control [slides]We consider how to extend FDR control to the online setting, and introduce the LORD algorithm. We also begin to review the Bayesian and frequentist approahces to decision-making.
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| 5 | Thursday 9/10/20 |
Frequentist and Bayesian Decision-Making, Privacy, Fairness [slides]
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| 4 | 6 | Tuesday 9/15/20 |
Review of Bayesian Modeling [slides]
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| 7 | Thursday 9/17/20 |
Graphical Models [slides]
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| 5 | 8 | Tuesday 9/22/20 |
Rejection Sampling and Markov Chains [slides]
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| 9 | Thursday 9/24/20 |
MCMC and Gibbs Sampling [slides]
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| 6 | 10 | Tuesday 9/29/20 |
Regression (Bayesian) [slides]
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| 11 | Thursday 10/1/20 |
Regression (Frequentist) [slides]
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| 7 | 12 | Tuesday 10/6/20 |
Bootstrap [slides]
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| 13 | Thursday 10/8/20 |
Causal Inference I [slides]
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| 8 | 14 | Tuesday 10/13/20 |
Causal Inference II [slides]
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| 15 | Thursday 10/15/20 |
Causal Inference II (continued)
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| 9 | 16 | Tuesday 10/20/20 |
Midterm
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| 17 | Thursday 10/22/20 |
Concentration Inequalities
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| 10 | 18 | Tuesday 10/27/20 |
Bandits I [slides]
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| 19 | Thursday 10/29/20 |
Bandits II
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| 11 | 20 | Tuesday 11/3/20 |
Matching Markets [slides]
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| 21 | Thursday 11/5/20 |
Game Theory [slides]
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| 12 | 22 | Tuesday 11/10/20 |
Reinforcement Learning I [slides]
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| 23 | Thursday 11/12/20 |
Reinforcement Learning II
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| 13 | 24 | Tuesday 11/17/20 |
Nonparametric Methods [slides]
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| 25 | Thursday 11/19/20 |
Generalization, Robustness, Distribution Shift I [slides]
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| 14 | 26 | Tuesday 11/24/20 |
Generalization, Robustness, Distribution Shift II [slides]
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| 27 | Thursday 11/26/20 |
Thanksgiving Break
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| 15 | 28 | Tuesday 12/1/20 |
Differential Privacy [slides]
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| 29 | Thursday 12/3/20 |
Wrap-up Lecture
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| 16 | 30 | Tuesday 12/8/20 |
RRR week
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| 31 | Thursday 12/10/20 |
RRR week
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| 17 | 32 | Tuesday 12/15/20 |
Final Exam
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