Data, Inference, and Decisions

This course develops the probabilistic foundations of inference in data science, and builds a comprehensive view of the modeling and decision-making life cycle in data science including its human, social, and ethical implications. Topics include: frequentist and Bayesian decision-making, permutation testing, false discovery rate, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals, causal inference, robustness, Thompson sampling, optimal control, Q-learning, differential privacy, fairness in classification, recommendation systems and an introduction to machine learning tools including decision trees, neural networks and ensemble methods.

This class is listed as STAT 102.

Important Information:

• When: Lectures Tuesdays and Thursdays from 2:00PM to 3:30PM. See below for discussions, labs, and office hours.
• Where: Remote instruction. See Piazza posts for corresponding Zoom links.
• What: See the lecture schedule
• News: We will post updates about the class on Piazza.

If you are requesting an extension for a HW or lab assignment (due to either DSP accommodations or other extenuating circumstances), please email any of the GSIs prior to the original deadline date. Please avoid emailing the professors.

Lab, Discussion, and Office Hours Schedules

See Piazza posts for corresponding Zoom links.

For official holidays see the academic calendar.

Prerequisites

While we are working to make this class widely accessible we currently require the following (or equivalent) prerequisites :

1. Principles and Techniques of Data Science: DS100 covers important computational and statistical skills that will be necessary for DS102.

2. Probability: Probability and Random Processes EECS126, or Concepts of Probability STAT134, or Probability for Data Science STAT140, or Probability and Risk Analysis for Engineers IEOR172. EECS126 and STAT140 are prefered. These courses cover the probabilistic tools that will form the underpinning for the concepts covered in DS102.

3. Math: Linear Algebra & Differential Equations Math54, or Linear Algebra MATH110, or both Designing Information Devices and Systems I EE16A and Designing Information Devices and Systems II EE16B, or Linear Algebra for Data Science Stat89a, or Introduction to Mathematical Physics PHYSICS89. We will need some basic concepts like linear operators, eigenvectors, derivatives, and integrals to enable statistical inference and derive new prediction algorithms.

Main Instructors

See Piazza posts for Zoom OH links.

Michael Jordan

OH: Tuesdays 3:30-4:30pm PT

(email)

Jacob Steinhardt

OH: Thursdays 3:30-4:30pm PT

(email)

TAs

See Piazza posts for corresponding Zoom links.

Mihaela Curmei

Disc: Wednesdays 12pm-1pm PT, 1pm-2pm PT

Lab: Mondays 12pm-2pm PT

OH: Tuesdays 1pm PT

(email)

Jake Soloff

Disc: Wednesdays 2pm-3pm PT, 3pm-4pm PT

Lab: Mondays 2pm-3pm PT, 4pm-5pm PT

Thursday 5pm PT

(email)

Yimeng (Kobe) Wang

Disc: Wednesdays 4pm-5pm PT

Lab: Mondays 3pm-4pm PT

OH: Fridays 10am PT

(email)

Clara Wong-Fannjiang

Disc: Wednesdays 9am-10am PT

Lab: TBD

(email)

Banghua Zhu

Disc: Wednesdays 10am-11am PT, 11am-12pm PT

Lab: Mondays 8am-10am PT

OH: Thursday 8:30am PT

(email)