Course information

Instructor: Jonathan Niles-Weed (jnw@cims.nyu.edu), Office hours: Friday 11 am-12 pm, zoom

Teaching Assistants:

  • Haoxiang Huang (recitation leader), Office hours: Tuesday 10 am-11 am, zoom
  • Aram-Alexandre Pooladian (grader)

Lecture

Friday 2-3:40 PM, 60 5th Avenue 150

Recitation

Friday 4:55-5:45 PM, GCASL 475

Note: In accordance with NYU policies, in-person attendance is expected for Fall 2021 classes. Until the end of the add/drop period (September 15), we will be be making recordings of the lectures and recitations available upon request.

Piazza

For announcements and questions, please sign up on Piazza.

Description

The goal of this course is to develop mathematical tools for analyzing statistical procedures.

Prerequisites

Probability, linear algebra, mathematical maturity (comfort with proofs)

Books

While there is no required textbook, a portion of the material for this class will be drawn from All of Statistics by Larry Wasserman.

This will be rigorous where possible, but several mathematically tricky issues will be ignored. A more rigorous treatment of some of the topics we cover is available in:

All three books are available for free via the links above with NYU credentials.

Lectures

Lecture notes will be written for each week’s lecture. Do not print them out, since they will be updated continuously throughout the semester. We will follow the following approximate schedule.

Unit 1: Asymptotics and non-asymptotics

  • Week 1: Concentration inequalities
  • Week 2: Maximal inequalities and uniform convergence
  • Week 3: Asymptotics

Unit 2: Classical statistical tasks

  • Week 4: Statistical modeling (regression, classification, and clustering)
  • Week 5: Estimation
  • Week 6: Testing
  • Week 7: Midterm exam
  • Week 8: Regularization

Unit 3: Applications and extension

  • Week 9: Monte-Carlo methods
  • Week 10: Non-parametric statistics
  • Week 11: Model selection and cross-validation
  • Week 12: Bayesian statistics
  • Week 13: Minimax lower bounds
  • Week 14: Causal inference

Homeworks

The homework assignments will be drawn from the list of exercises at the end of each chapter of the lecture notes. Homeworks are due Thursdays at 5 pm Eastern time via Gradescope (entry code: 3Y236X). Each student may request one homework extension over the course of the semester, with no excuse necessary. Further requests will not be considered.

You may work with other students, however you must a) write solutions to the homework yourself and b) list the names of the students you collaborated with. If you consult any other sources (printed or online), you must cite those in your homework as well. Any violation of these policies will be considered cheating.

  • HW 1 (due 9/9): Chapter 1, Exercises 1-6
  • HW 2 (due 9/16): Chapter 2, Exercises 1-4
  • HW 3 (due 9/23): Chapter 3, Exercises 1-5

Grading

40% Homework + 30% Midterm + 30% Final project

Homework

There will be approximately 10 homework assignments over the course of the semester. The lowest homework score will be dropped.

Exams

There will be an in-class midterm exam on October 15

Final Project

In lieu of a final exam, this course will have a final project involving reading and summarizing a recent paper (or papers) of statistical interest. More details will be available later in the semester.

Cheating

NYU policy prescribes strong punishments for students caught cheating. The course staff will be carefully monitoring assignments and exams for signs of academic dishonesty.