Statistics and machine learning in high dimensions

Om kursen

The explosion in the volume of data collected in all scientific disciplines and in industry requires students interested in statistical analyses and machine-learning and signal-processing algorithms to acquire more sophisticated probability tools than the ones taught in basic probability courses.

This course provides an introduction to the area of high-dimensional statistics, which deals with large scale problems where both the number of parameters and the sample size is large.

The course covers fundamental tools for the analysis of random vectors, random matrices and random projections, such as tail bounds and concentration inequalities.

It further provides concrete applications of such tools in the context of generalization-error analyses in statistical learning theory, sparse linear model, and matrix models with rank constraints.

Additional information
The course is also part of the master program in information and communication technologies (MPICT). Additional information is available in the student portal
Application code: 13117

Note! For application for PhD students please register here:
Form Application for first/second cycle course for doctoral student at Chalmers


Giuseppe Durisi