Concept
Ordered logit
Cours associés (23)
CIVIL-226: Introduction to machine learning for engineers
Machine learning is a sub-field of Artificial Intelligence that allows computers to learn from data, identify patterns and make predictions. As a fundamental building block of the Computational Thinki
ENV-167: Introduction to environmental engineering
Key themes in environmental science and engineering will be show-cased, with examples - from equator to the poles - including atmospheric processes and climate change, water quality, energy resources
PHYS-467: Machine learning for physicists
Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practi
CS-433: Machine learning
Machine learning methods are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and pr
FIN-407: Machine learning in finance
This course aims to give an introduction to the application of machine learning to finance, focusing on the problems of portfolio optimization and hedging, as well as textual analysis. A particular fo
MATH-236: Probability and statistics II
Linear statistical methods, analysis of experiments, logistic regression.
MATH-493: Applied biostatistics
This course covers topics in applied biostatistics, with an emphasis on practical aspects of data analysis using R statistical software. Topics include types of studies and their design and analysis,
ENG-209: Data science for engineers with Python
Ce cours est divisé en deux partie. La première partie présente le langage Python et les différences notables entre Python et C++ (utilisé dans le cours précédent ICC). La seconde partie est une intro
EE-556: Mathematics of data: from theory to computation
This course provides an overview of key advances in continuous optimization and statistical analysis for machine learning. We review recent learning formulations and models as well as their guarantees
MATH-413: Statistics for data science
Statistics lies at the foundation of data science, providing a unifying theoretical and methodological backbone for the diverse tasks enountered in this emerging field. This course rigorously develops