About this Course

Data, Inference, and Decisions

This course develops the probabilistic foundations of inference in data science. It builds a comprehensive view of the decision-making and modeling 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.

Announcements

All course announcements will be made on Piazza.

Meeting Times

The course structure this semester is different from previous semesters as we adapt to remote learning. For changes to grading, see the grading tab.

Lecture: Each lecture will be released as a playlist of short, 3-15 minute videos totalling about an hour (total length will vary from lecture to lecture), linked to from the main course page. We ask that you view each lecture’s video before the corresponding discussion session. We’ll have Piazza threads for each lecture where you can ask questions.

Discussion session:

Lab: There will be no lab meetings: we ask that you complete lab assignments on your own time and use office hours to get help from course staff.

Office Hours Schedules

Please 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.

Ramesh Sridharan
Ramesh Sridharan

OH: TBA

(email)

Yan Shuo Tan
Yan Shuo Tan

OH: TBA

(email)

TAs

See Piazza posts for corresponding Zoom OH links.

Erika Mack
Erika Mack

OH: TBA

(email)

Ewen Dai
Ewen Dai

OH: TBA

(email)

Ryan Roggenkemper
Ryan Roggenkemper

OH: TBA

(email)

Xingyu Jin
Xingyu Jin

OH: TBA

(email)