We discuss a set of topics that are important for the understanding of modern data science but that are typically not taught in an introductory ML course. In particular we discuss fundamental ideas and techniques that come from probability, information the ...
This hands-on course covers tools and methods used by data scientists, from researching solutions to scaling prototypes on Spark clusters. Students engage with the full data engineering and data science pipeline, from data acquisition to extracting insight ...
This seminar teaches the participants to use advanced Python concepts for writing easier to read, more flexible and faster code.
It teaches concepts in a hands-on and tangible fashion, providing example use cases that all applied mathematicians can relate ...
A rigorous introduction to the statistical analysis of random functions and associated random operators. Viewing random functions either as random Hilbert vectors or as stochastic processes, we will see the interplay between nonparametrics and multivariate ...
The course gives (1) a review of different types of numerical models of control of locomotion and movement in animals, from fish to humans, (2) a presentation of different techniques for designing models, and (3) an analysis of the use and testing of those ...
Memory corruption and type safety flaws dominate the threat landscape. We will approach current research
from three dimensions: sanitization (finding flaws through runtime monitors); fuzzing (testing software
automatically); and mitigation (protecting soft ...