Concept
Model selection
Cours associés (32)
EE-512: Applied biomedical signal processing
The goal of this course is twofold: (1) to introduce physiological basis, signal acquisition solutions (sensors) and state-of-the-art signal processing techniques, and (2) to propose concrete examples
MATH-516: Applied statistics
The course will provide an overview of everyday challenges in applied statistics through case studies. Students will learn how to use core statistical methods and their extensions, and will use comput
BIO-322: Introduction to machine learning for bioengineers
Students understand basic concepts and methods of machine learning. They can describe them in mathematical terms and can apply them to data using a high-level programming language (julia/python/R).
ENV-524: Hydrological risks and structures
Le cours est une introduction à l'hydrologie statistique avec un accent mis sur l'hydrologie des petits bassins-versants de montagne. Le cours comprend une introduction générale à la gestion des risqu
FIN-417: Quantitative risk management
This course is an introduction to quantitative risk management that covers standard statistical methods, multivariate risk factor models, non-linear dependence structures (copula models), as well as p
CS-423: Distributed information systems
This course introduces the foundations of information retrieval, data mining and knowledge bases, which constitute the foundations of today's Web-based distributed information systems.
MATH-562: Statistical inference
Inference from the particular to the general based on probability models is central to the statistical method. This course gives a graduate-level account of the main ideas of statistical inference.
PHYS-442: Modeling and design of experiments
In the academic or industrial world, to optimize a system, it is necessary to establish strategies for the experimental approach. The DOE allows you to choose the best set of measurement points to min
MGT-302: Introduction to data driven business analytics
This course focuses on methods and algorithms needed to apply machine learning with an emphasis on applications
in business analytics
MATH-236: Probability and statistics II
Linear statistical methods, analysis of experiments, logistic regression.