This course provides the students with 1) a set of theoretical concepts to understand the machine learning approach; and 2) a subset of the tools to use this approach for problems arising in mechanical engineering applications. ...
In this course, students learn to design and master algorithms and core concepts related to inference and learning from data and the foundations of adaptation and learning theories with applications.
Biology is becoming more and more a data science, as illustrated by the explosion of available genome sequences. This course aims to show how we can make sense of such data and harness it in order to understand biological processes in a quantitative way. ...
This course aims at giving a broad overview of Bayesian inference, highlighting how the basic Bayesian paradigm proceeds, and the various methods that can be used to deal with the computational issues that plague it. This course represents a 70-30 split of ...