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 1/18/22	Course Overview, Making Decisions Under Uncertainty Artificial Intelligence — The Revolution Hasn’t Happened Yet (Optional) Discussion of the above article by nine commentators 1, 2, 3, 4, 5, 6, 7 (Optional video) Prof. Michael Jordan’s perspective on data science (Optional) What are the most important statistical ideas of the past 50 years?	Slides Annotated	Videos
1	2	Thursday 1/20/22	Binary Decision-Making	Slides Annotated	Videos		Lab 0
2	3	Tuesday 1/25/22	P-Values and Multiple Hypothesis Testing	Slides (Optional) The Garden of Forking Paths Demo Notebook	Videos	Discussion 1		HW 1
2	4	Thursday 1/27/22	Online False Discovery Rate Control & ROC curves	Slides Demo Notebook	Videos		Lab 1
3	5	Tuesday 2/1/22	Frequentist vs. Bayesian Decision-Making	Slides Tablet notes from lecture Typed notes	Videos	Discussion 2
3	6	Thursday 2/3/22	Overview of Bayesian Modeling	Slides Notes and demo Tablet notes from lecture	Videos		Lab 2
4	7	Tuesday 2/8/22	Graphical Models	Slides Slides (annotated) Demo	Videos	Discussion 3		HW 2
4	8	Thursday 2/10/22	Rejection Sampling and Markov Chains	Slides Notes and demo	Videos		Lab 3
5	9	Tuesday 2/15/22	Markov Chain Monte Carlo (MCMC) and Gibbs Sampling	Slides Gibbs sampling demo	Videos	Discussion 4
5	10	Thursday 2/17/22	Regression and GLMs	Slides Demo	Videos		Lab 4 Walkthrough
6	11	Tuesday 2/22/22	Model checking for GLMs	Slides Demo	Videos
6		Thursday 2/24/22	Midterm 1				Lab 5
7	12	Tuesday 3/1/22	Uncertainty quantification for GLMs	Slides Demo	Videos	Discussion 5		HW 3
7	13	Thursday 3/3/22	Nonparametric Methods and Neural Networks Required: Statistical Modeling, The Two Cultures, by Leo Breiman (pp 1-11)	Slides Demo	Videos		Lab 6
8	14	Tuesday 3/8/22	Neural Networks and Interpretability Backpropagation in more detail	Slides Demo	Videos	Discussion 6
8	15	Thursday 3/10/22	Causal Inference I, Problems with association	Slides Notes 1: Association, correlation, and causation Notes 2: Quantifying association Notes 3: Causality and Potential Outcomes	Videos		Lab 7
9	16	Tuesday 3/15/22	Causal Inference II, Randomized experiments	Required reading: Discrimination in hiring article (pdf) and skim the paper Slides Notes: Causality and Potential Outcomes Notes: Instrumental Variables	Videos	Discussion 7		HW 4
9	17	Thursday 3/17/22	Causal Inference III, Observational studies	Slides Notes: Instrumental Variables	Videos		Lab 8
10	18	Tuesday 3/22/22	Spring Break
10	19	Thursday 3/24/22	Spring Break
11	20	Tuesday 3/29/22	Reinforcement Learning I	Slides Demo	Videos	Discussion 8
11	21	Thursday 3/31/22	Reinforcement Learning II	Slides Demo	Videos		Lab 9
12	22	Tuesday 4/5/22	Concentration inequalities	Slides Notes Annotated Slides	Videos	Discussion 9		HW5
12	23	Thursday 4/7/22	Bandits I	Slides Demo Annotated Slides	Videos		Lab 10
13	24	Tuesday 4/12/22	Bandits II	Slides Annotated Slides	Videos
13		Thursday 4/14/22	Midterm 2
14	25	Tuesday 4/19/22	Differential Privacy	Slides Annotated	Videos	Discussion 10		HW6
14	26	Thursday 4/21/22	Robustness and Generalization	Slides	Videos		Lab 11
15	27	Tuesday 4/26/22	Matching Markets and Game Theory	Slides	Videos	Discussion 11
15	28	Thursday 4/28/22	Course Wrap-up	Slides	Videos
16	29	Tuesday 5/3/22	RRR week
16	30	Thursday 5/5/22	RRR week

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

Midterm 1

Uncertainty quantification for GLMs

Nonparametric Methods and Neural Networks

Neural Networks and Interpretability

Causal Inference I, Problems with association

Causal Inference II, Randomized experiments

Causal Inference III, Observational studies

Spring Break

Spring Break

Reinforcement Learning I

Reinforcement Learning II

Concentration inequalities

Bandits I

Bandits II

Midterm 2

Differential Privacy

Robustness and Generalization

Matching Markets and Game Theory

Course Wrap-up

RRR week

RRR week