MATH-685 / 2 credits

Teacher(s): Panaretos Victor, Sarkar Soham

Language: English

Remark: Fall semester - The course will be mathematically rigorous. The goal is to make things as comprehensive as possible.


Frequency

Only this year

Summary

This course is intended to give a brief overview of how to prove consistency results in nonparametric regression. In particular, we will focus on least-square regression estimators. Some connections to the empirical risk minimization (ERM) problem will be discussed from time to time.

Content

Learning Prerequisites

Required courses

Basic knowledge of probability and statistics. Familiarity with asymptotic theory will be assumed. It is recommended to be familiar with the basics of regression.

In the programs

  • Number of places: 20
  • Exam form: Oral presentation (session free)
  • Subject examined: Learning Theory of Nonparametric Regression
  • Lecture: 24 Hour(s)

Reference week