Syllabus

This syllabus is under development and is subject to change. Due dates for homework and lab assignments will be announced later.

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 08/23/22	No class
1	2	Thursday 08/25/22	Course Overview, Making Decisions Under Uncertainty	Slides (Optional reading) Artificial Intelligence — The Revolution Hasn’t Happened Yet (Optional reading) What are the most important statistical ideas of the past 50 years?	Videos
2	3	Tuesday 08/30/22	Binary Decision-Making	Slides (Optional reading) The Garden of Forking Paths	Videos	Disc. 1 Soln.	Lab 0
2	4	Thursday 09/01/22	P-Values and Multiple Hypothesis Testing	Slides Demo notebook	Videos			HW 1 PDF
3	5	Tuesday 09/06/22	Online False Discovery Rate Control & ROC curves	Slides Demo notebook	Videos	Disc. 2 Soln.	Lab 1
3	6	Thursday 09/08/22	Frequentist vs. Bayesian Decision-Making	Slides Lecture Notes	Videos
4	7	Tuesday 09/13/22	Overview of Bayesian Modeling	Lecture Notes	Videos	Disc. 3 Soln.	Lab 2
4	8	Thursday 09/15/22	Graphical Models	Slides Slides (annotated) Demo notebook	Videos			HW 2 PDF
5	9	Tuesday 09/20/22	Rejection Sampling	Slides Lecture notes	Videos	Disc. 4 Soln.	Lab 3
5	10	Thursday 09/22/22	Markov Chain Monte Carlo (MCMC) and Gibbs Sampling	Slides Lecture notes	Videos
6	11	Tuesday 09/27/22	Regression and GLMs	Regression review explainer GLM demo notebook	Videos	Disc. 5 Soln.	Lab 4
6	12	Thursday 09/29/22	Uncertainty quantification and model checking for GLMs (Bayesian)	Slides Demo	Videos			HW 3 PDF
7	13	Tuesday 10/04/22	Uncertainty quantification and model checking for GLMs (Frequentist)	Slides Demo	Videos	Disc. 6 Soln. Past MT1 Review.
7		Thursday 10/06/22	Midterm 1
8	14	Tuesday 10/11/22	Nonparametric Methods and Neural Networks	Nonparametric methods notes and demo Neural networks and backpropagation notes and demo	Videos		Lab 5
8	15	Thursday 10/13/22	Interpretability, Comparisons, and Summary	Slides Demo (Optional reading) Statistical Modeling, The Two Cultures, by Leo Breiman, pg 1-11 (Optional video) Prof. Cynthia Rudin on explainability	Videos
9	16	Tuesday 10/18/22	Causal Inference I, Problems with association	Slides Notes 1: Association, correlation, and causation Notes 2: Quantifying association Notes 3: Causality and Potential Outcomes Required reading for next time: Discrimination in hiring article (pdf) and skim the paper	Videos	Disc. 7 Soln.	Lab 6
9	17	Thursday 10/20/22	Causal Inference II, Randomized experiments	Required reading: Discrimination in hiring article (pdf) and skim the paper Slides Notes: ATE and randomized experiments Notes: Instrumental variables	Videos			HW 4 PDF
10	18	Tuesday 10/25/22	Causal Inference III, Observational studies	Slides	Videos	Disc. 8 Soln.	Lab 7
10	19	Thursday 10/27/22	Concentration inequalities	Notes Handwritten notes	Videos
11	20	Tuesday 11/01/22	Bandits I	Notes (fixed) slides	Videos	Disc. 9 Soln.	Lab 8
11	21	Thursday 11/03/22	Bandits II	Slides Notes Demo Handwritten notes	Videos			HW 5 PDF
12	22	Tuesday 11/08/22	Reinforcement Learning I	Slides Demo	Videos	Disc. 10 Soln.	Lab 9
12	23	Thursday 11/10/22	Reinforcement Learning II	Slides Handwritten Notes	Videos
13	24	Tuesday 11/15/22	Differential Privacy	Note this lecture is a full video by Jacob from Fall 2022 Slides	Videos		Lab 10
13		Thursday 11/17/22	Midterm 2
14	25	Tuesday 11/22/22	Robustness and Generalization	Note this lecture is a full video by Ramesh from Spring 2022 Slides	Videos
14	26	Thursday 11/24/22	Thanksgiving
15	27	Tuesday 11/29/22	Matching Markets and Game Theory	Note this is lecture is a full video by Ramesh from Fall 2021 and covers two lectures Slides	Videos
15	28	Thursday 12/01/22	Course Wrap-up

Syllabus

No class

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

Markov Chain Monte Carlo (MCMC) and Gibbs Sampling

Regression and GLMs

Uncertainty quantification and model checking for GLMs (Bayesian)

Uncertainty quantification and model checking for GLMs (Frequentist)

Midterm 1

Nonparametric Methods and Neural Networks

Interpretability, Comparisons, and Summary

Causal Inference I, Problems with association

Causal Inference II, Randomized experiments

Causal Inference III, Observational studies

Concentration inequalities

Bandits I

Bandits II

Reinforcement Learning I

Reinforcement Learning II

Differential Privacy

Midterm 2

Robustness and Generalization

Thanksgiving

Matching Markets and Game Theory

Course Wrap-up