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	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
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	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
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 Whiteboard notes 1 Whiteboard notes 2	Videos
7		Tuesday 3/2/21	Midterm 1					Lab 05	HW 04 HW 04 Solutions
7	13	Thursday 3/4/21	Causal Inference I, Problems with association	Slides Whiteboard notes	Videos		Lecture worksheet Solutions
8	14	Tuesday 3/9/21	Causal Inference II, Randomized experiments	Slides Whiteboard notes	Videos		Lecture worksheet Solutions	Lab 06
8	15	Thursday 3/11/21	Causal Inference III, Observational studies	Slides Whiteboard notes	Videos		Lecture worksheet Solutions
9	16	Tuesday 3/16/21	Causal Inference IV, Instrumental variables	Lecture notes	Videos		Lecture worksheet Solutions	Lab 07	HW 05 HW 05 Solutions
9	17	Thursday 3/18/21	Concentration inequalities	Lecture notes	Videos		Lecture worksheet Solutions
10	18	Tuesday 3/30/21	Bandits I	Lecture notes Demo	Videos		Lecture worksheet Solutions	Lab 08
10	19	Thursday 4/1/21	Bandits II	Lecture notes Demo	Videos		Lecture worksheet Solutions
11	20	Tuesday 4/6/21	Nonparametric Methods and Neural Networks	(Required reading) Statistical Modeling, The Two Cultures, by Leo Breiman Notes on nonparametric methods Notes on neural networks and backpropagation	Videos		Lecture worksheet	Lab 09	HW 06 HW 06 Solutions
11	21	Thursday 4/8/21	Neural Networks and Interpretability	Notes on neural networks and backpropagation Interpretability slides (not comprehensive) Interpretability demo (not comprehensive) (Optional talk) Prof. Cynthia Rudin on Explainability	Videos		Lecture worksheet
12		Tuesday 4/13/21	Midterm 2 Review
12		Thursday 4/15/21	Midterm 2
13	22	Tuesday 4/20/21	Reinforcement Learning	Notes Slides	Videos		Lecture worksheet Solutions	Lab 10
13	23	Thursday 4/22/21	Robustness and generalization		Videos		Lecture worksheet		HW 07 HW 07 Solutions
14	24	Tuesday 4/27/21	Differential Privacy		Videos		Lecture worksheet	Lab 11
14	25	Thursday 4/29/21	Matching Markets and Game Theory / Course Wrap-up

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, Problems with association

Causal Inference II, Randomized experiments

Causal Inference III, Observational studies

Causal Inference IV, Instrumental variables

Concentration inequalities

Bandits I

Bandits II

Nonparametric Methods and Neural Networks

Neural Networks and Interpretability

Midterm 2 Review

Midterm 2

Reinforcement Learning

Robustness and generalization

Differential Privacy

Matching Markets and Game Theory / Course Wrap-up