Syllabus

This syllabus is under development and is subject to change. 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 the course email (data102@berkeley.edu) prior to the original deadline date. Please avoid emailing the professors.

Show all lecture descriptions

Week	Lecture	Date	Topic	Slides	Lecture	Discussion Materials	Lab	Homework
1	1	Tuesday 8/24/21	(No Class)
1	2	Thursday 8/26/21	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?	Slides	Videos
2	3	Tuesday 8/31/21	Binary Decision-Making (Optional) The Garden of Forking Paths	Slides	Videos	Disc 1	Lab 0
2	4	Thursday 9/2/21	P-Values and Multiple Hypothesis Testing	Slides Demo Notebook	Videos			HW 1 released
3	5	Tuesday 9/7/21	Error Rates for Decisions	Slides Demo Notebook Why B-H controls FDR (video) Optional note Why LORD controls FDR (part 2)	Videos	Disc 2	Lab 1
3	6	Thursday 9/9/21	Frequentist vs. Bayesian Decision-Making	Slides Lecture notes	Videos
4	7	Tuesday 9/14/21	Overview of Bayesian Modeling	Slides Demo Notebook	Videos	Disc 3	Lab 2
4	8	Thursday 9/16/21	Graphical Models	Slides	Videos			HW 2 released
5	9	Tuesday 9/21/21	Rejection Sampling and Markov Chains	Slides demo	Videos	Disc 4	Lab 3
5	10	Thursday 9/23/21	Markov Chain Monte Carlo (MCMC) and Gibbs Sampling	Slides demo	Videos
6	11	Tuesday 9/28/21	Regression and GLMs	Slides GLM demo notebook Regression review explainer	Videos	Disc 5	Lab 4
6	12	Thursday 9/30/21	Model checking for GLMs	Slides demo	Videos
7		Tuesday 10/5/21	Midterm I
7	13	Thursday 10/7/21	Uncertainty quantification for GLMs	Slides demo Required reading for 10/12, pgs 1-11, Statistical Modeling, The Two Cultures, by Leo Breiman	Videos			HW 3 released
8	14	Tuesday 10/12/21	Nonparametric Methods and Neural Networks	Slides Nonparametric methods notes and demo Neural networks notes and demo	Videos	Disc 6	Lab 5
8	15	Thursday 10/14/21	Neural Networks and Interpretability	Slides Neural networks notes and demo Interpretability demo (demo only, no notes) (Optional video) Prof. Cynthia Rudin on Explainability	Videos
9	16	Tuesday 10/19/21	Causal Inference I	Slides Notes 1: Association, correlation, and causation Notes 2: Quantifying association Notes 3: Causality and Potential Outcomes See Ed for additional resources on causal inference	Videos	Disc 7	Lab 6
9	17	Thursday 10/21/21	Causal Inference II	Slides Notes: Causality and potential outcomes Notes: Instrumental variables	Videos			HW 4 released
10	18	Tuesday 10/26/21	Causal Inference III	Slides	Videos		Lab 7
10	19	Thursday 10/28/21	Concentration inequalities	Lecture notes notebook Written notes	Videos
11	20	Tuesday 11/2/21	Bandits I	Lecture notes Written notes Demo	Videos		Lab 8
11	21	Thursday 11/4/21	Bandits II	Lecture notes	Videos
12	22	Tuesday 11/9/21	Reinforcement Learning I	Slides Notes	Videos		Lab 9
12	23	Thursday 11/11/21	Holiday
13	24	Tuesday 11/16/21	Reinforcement Learning II	Notes Whiteboard notes Whiteboard notes (2020)	Videos		Lab 10
13		Thursday 11/18/21	Midterm II
14	25	Tuesday 11/23/21	Differential Privacy	Slides	Videos
14	26	Thursday 11/25/21	Thanksgiving Break
15	27	Tuesday 11/30/21	Robustness and generalization	Slides	Videos		Lab 11
15	28	Thursday 12/2/21	Matching Markets and Game Theory / Course Wrap-up	Slides	Videos
16	29	Tuesday 12/7/21	RRR week
16	30	Thursday 12/9/21	RRR week

Syllabus

(No Class)

Course Overview, Making Decisions Under Uncertainty

Binary Decision-Making

P-Values and Multiple Hypothesis Testing

Error Rates for Decisions

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

Midterm I

Uncertainty quantification for GLMs

Nonparametric Methods and Neural Networks

Neural Networks and Interpretability

Causal Inference I

Causal Inference II

Causal Inference III

Concentration inequalities

Bandits I

Bandits II

Reinforcement Learning I

Holiday

Reinforcement Learning II

Midterm II

Differential Privacy

Thanksgiving Break

Robustness and generalization

Matching Markets and Game Theory / Course Wrap-up

RRR week

RRR week