Syllabus

This syllabus is under development and is subject to change. Unless otherwise noted, lecture videos will be released at least two days before the corresponding discussion session. Due dates for homework and lab assignments will be announced shortly.

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.

Show all lecture descriptions

Week	Lecture	Date	Topic	Slides	Lecture	Discussion Recordings	Discussion Materials	Lab	Homework
1	1	Tuesday 1/19/21	Course Overview, Making Decisions Under Uncertainty Artificial Intelligence — The Revolution Hasn’t Happened Yet (Optional) Discussion by nine commentators 1, 2, 3, 4, 5, 6, 7 (Optional) Dr. AI or: How I Learned to Stop Worrying and Love Economics (Optional) What are the most important statistical ideas of the past 50 years?	Fall20 Slides	Videos	Video
1	2	Thursday 1/21/21	Binary Decision-Making	Fall20 Slides	Videos	Video	Lecture worksheet Solutions
2	3	Tuesday 1/26/21	P-Values and Multiple Hypothesis Testing	Fall20 Slides	Videos	Video	Lecture worksheet Solutions Slides	Lab 01 Lab 01 Solutions	HW 01 HW 01 Solutions
2	4	Thursday 1/28/21	Online False Discovery Rate Control & ROC curves	Fall20 Slides ROC notebook	Videos	Video	Lecture worksheet Solutions
3	5	Tuesday 2/2/21	Frequentist vs. Bayesian Decision-Making	Fall20 Slides Spring20 Notes	Videos	Video	Lecture worksheet Solutions	Lab 02 Lab 02 Solutions
3	6	Thursday 2/4/21	Overview of Bayesian Modeling	Demo notebook	Videos	Video	Lecture worksheet
4	7	Tuesday 2/9/21	Graphical Models	Lecture slides	Videos	Video	Lecture worksheet	Lab 03 Lab 03 Solutions	HW 02 HW 02 Solutions
4	8	Thursday 2/11/21	Rejection Sampling and Markov Chains	Lecture slides	Videos		Lecture worksheet
5	9	Tuesday 2/16/21	Markov Chain Monte Carlo (MCMC) and Gibbs Sampling	Demo notebook	Videos		Lecture worksheet	Lab 04 Lab 04 Solutions
5	10	Thursday 2/18/21	Regression and GLMs	Regression review explainer notebook Generalized Linear Model notebook	Videos		Lecture worksheet
6	11	Tuesday 2/23/21	Model checking for GLMs	Slides Demo notebook 1 Demo notebook 2 Demo notebook 3	Videos		Lecture worksheet Solutions		HW 03 HW 03 Solutions
6	12	Thursday 2/25/21	Uncertainty quantification for GLMs	Slides Demo notebook	Videos
7		Tuesday 3/2/21	Midterm 1					Lab 05
7	13	Thursday 3/4/21	Causal Inference I	Slides Whiteboard notes	Videos		Lecture worksheet
8	14	Tuesday 3/9/21	Causal Inference II
8	15	Thursday 3/11/21	Causal Inference III
9	16	Tuesday 3/16/21	Concentration Inequalities
9	17	Thursday 3/18/21	Bandits I
10	18	Tuesday 3/30/21	Bandits II
10	19	Thursday 4/1/21	Generalization, Robustness, Distribution Shift I
11	20	Tuesday 4/6/21	Interpretability
11	21	Thursday 4/8/21	Reinforcement Learning I
12	22	Tuesday 4/13/21	Reinforcement Learning II
12		Thursday 4/15/21	Midterm 2
13	23	Tuesday 4/20/21	Game Theory and Matching Markets
13	24	Thursday 4/22/21	Differential Privacy
14	25	Tuesday 4/27/21	Nonparametric Methods
14	26	Thursday 4/29/21	Generalization, Robustness, Distribution Shift II

Syllabus

Course Overview, Making Decisions Under Uncertainty

Binary Decision-Making

P-Values and Multiple Hypothesis Testing

Online False Discovery Rate Control & ROC curves

Frequentist vs. Bayesian Decision-Making

Overview of Bayesian Modeling

Graphical Models

Rejection Sampling and Markov Chains

Markov Chain Monte Carlo (MCMC) and Gibbs Sampling

Regression and GLMs

Model checking for GLMs

Uncertainty quantification for GLMs

Midterm 1

Causal Inference I

Causal Inference II

Causal Inference III

Concentration Inequalities

Bandits I

Bandits II

Generalization, Robustness, Distribution Shift I

Interpretability

Reinforcement Learning I

Reinforcement Learning II

Midterm 2

Game Theory and Matching Markets

Differential Privacy

Nonparametric Methods

Generalization, Robustness, Distribution Shift II