This course is neither an introduction to the mathematics of statistics nor an introduction to a statistics program such as R. The aim of the course is to understand statistics from its experimental design and to avoid common pitfalls of statistical reason ...
This course discusses quantitatively some important and generic performance and reliability issues that affect the behavior of manufacturing systems and supply chains. ...
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 course provides an introduction to the physical phenomenon of turbulence, its probabilistic description and modeling approaches including RANS and LES. Students are equipped with the basic knowledge to tackle complex flow problems in science and engin ...
This course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and implement methods to analyze diverse data types, such as imag ...
Seminar course which allows students to attend, critically examine, and review civil/environmental seminar. - Practice transferable skills, including active listening and broad scientific critical thinking - Presentation and communication skills ...
This course provides in-depth understanding of the most fundamental algorithms in statistical pattern recognition or machine learning (including Deep Learning) as well as concrete tools (as Python source code) to PhD students for their work. ...