Concept
Modèle graphique
Cours associés (15)
EE-612: Fundamentals in statistical pattern recognition
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 sou
PHYS-512: Statistical physics of computation
The students understand tools from the statistical physics of disordered systems, and apply them to study computational and statistical problems in graph theory, discrete optimisation, inference and m
EE-613: Machine Learning for Engineers
The objective of this course is to give an overview of machine learning techniques used for real-world applications, and to teach how to implement and use them in practice. Laboratories will be done i
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
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
CS-421: Machine learning for behavioral data
Computer environments such as educational games, interactive simulations, and web services provide large amounts of data, which can be analyzed and serve as a basis for adaptation. This course will co
MGT-448: Statistical inference and machine learning
This course aims to provide graduate students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The course covers topi
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
EE-470: Power systems dynamics
This course focuses on the dynamic behavior of a power system. It presents the basic definitions, concepts and models for angular stability analysis with reference to transient stability, steady state
MATH-336: Randomization and causation
This course covers formal frameworks for causal inference. We focus on experimental designs, definitions of causal models, interpretation of causal parameters and estimation of causal effects.