• Link to Canvas course site
• If you have asked for an add code: you have been already added to the waitlist. Otherwise, before emailing me, please check carefully the information on this page, and let me know in your email that you satisfy them. I will give add codes the week classes start.

What will the course be about?
The most successful deployed statistical inference systems today which work with big data are non-parametric. In a worlds where the amounts of data collected increase continually, non-parametric statistics is coming of age, through its ability to adapt the model to the data complexity. This course will give an overview of the NP toolbox, and will shed light on the understanding of the why's in NP statistics.

Learning goals

• Understanding the foundational properties of NP models
• Ability to understand these properties when expressed in mathematical/quantitative terms
• Ability to implement and use NP models on real data sets
• Ability to interpret NP models, and to validate the statistical inferences based on them
• Ability to apply the knowledge gained in a data analysis project.
• Ability to communicate about the properties of NP models, their behavior on data and their implementation

Who is this class for?
Statistics and Biostatistics MS and PhD students.
Capacity permitting, the class is open to other graduate students with an interest in statistics, algorithms and computing, in particular to students involved in Machine Learning research across campus.

Optional Textbooks are listed here

Prerequisites

• (STAT 502 and STAT 504) or (BIOST 514 and BIOST 515)
• In more detail:
• Fundamentals of statistics, such as regression, classification, density estimation
• Calculus and linear algebra: partial derivatives, gradient, Hessian, the chain rule, vectors and matrices, matrix multiplications, eigenvalues and eigenvectors, positive definite matrices
• [Algorithms and data structure at a basic level (arrays, lists, sets, O( ) notation) are helpful]
• Medium (beyond beginner) ability with a computer programming language (like C, C++, Java or Matlab, Splus, R) at the level of STAT 534

Instructor: Marina Meila   mmp at stat dot washington dot edu

Lectures: Mondays, Wednesdays 10-11:20, in THO 125. This is an in-person class, a small number of on-line, pre-announced lectures are possible. The in person lectures will not be recorded, but the on-line lectures (if any) will be recorded.

Office hours: TBA on-line (TB confirmed)

Course home page: http://www.stat.washington.edu/mmp/courses/527/spring23 (this page)

Course Canvas site: is here