Concept

Conditional probability

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CS-702: Topics in Machine Learning for Education

MSE-668: Non-destructive methods for industry and research

Basic knowledge ofthe classical non-destructive testing methods as they are used today in industrial applications and the advanced (mosily imaginé) technologies used for the analysis of materials and

MATH-485: Introduction to stochastic PDEs

Stochastic PDEs are used to model systems that are spatially extended and include a random component. This course gives an introduction to this topic, including some general measure theory, some Gauss

MICRO-428: Metrology

The course deals with the concept of measuring in different domains, particularly in the electrical, optical, and microscale domains. The course will end with a perspective on quantum measurements, wh

COM-406: Foundations of Data Science

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COM-102: Advanced information, computation, communication II

Text, sound, and images are examples of information sources stored in our computers and/or communicated over the Internet. How do we measure, compress, and protect the informatin they contain?

MGT-416: Causal inference

Students will learn the core concepts and techniques of network analysis with emphasis on causal inference. Theory and application will be balanced, with students working directly with network data th

ENG-466: Distributed intelligent systems

The goal of this course is to provide methods and tools for modeling distributed intelligent systems as well as designing and optimizing coordination strategies. The course is a well-balanced mixture

MATH-496: Computational linear algebra

This is an introductory course to the concentration of measure phenomenon - random functions that depend on many random variables tend to be often close to constant functions.

MATH-519: Topics in high-dimensional probability

This is a theoretical course about probability in high dimensions. We will look at some mathematical phenomena appearing as the number of random variables grows large - e.g. concentration of measure o