Lectures: Tuesdays, Thursdays, 12:301:45PM, in CIWW (Courant Institute) 201.
Office hours: Tuesdays 10:0011:00AM, Wednesdays 12:301:30pm, Thursdays 2:003:00PM Office WWH926.
If needed, possibility to set up appointments by email (
thomasl@math.nyu.edu)
Recitation sessions: Fridays, 2:003:15PM in CIWW 201. T.A.
Alexisz Gaál (Office WWH830).
Course description:
An introduction to statistics, on the mathematical side.
Prerequisites: Theory of probability (UA.0233). Basic linear algebra can help, but is not mandatory.
Textbook:
"All of statistics", by L. Wasserman.
Available for free (PDF/EPUB format) from the publisher via the NYU network. See also
the book webpage for errata and data.
Other references:
 The excellent material (slides, videos, etc.) of the MIT class "Statistics for applications" is accessible online. There is a large intersection with our class (perhaps a bit more "applied" and less "technical").
 If you are interested by an introduction to "data science" in Python this is a great book (there are of course many other resources online).

This book is a very good, nontechnical introduction to "statistical learning"

This (Stochastics by H.O. Georgii) could be another textbook, more technical than the one we use.
This (Introduction to Mathematical Statistics and Its Applications, by Larsen and Marx) is a more "userfriendly" textbook, that can be interesting to look at.
Grading: Homework (25%), Midterm (35%), Final (40%)