PHYS-467: Machine learning for physicistsMachine 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 learningMachine 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
MICRO-401: Machine learning programmingThis is a practice-based course, where students program algorithms in machine learning and evaluate the performance of the algorithm thoroughly using real-world dataset.
FIN-407: Machine learning in financeThis 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
PHYS-512: Statistical physics of computationThe 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 EngineersThe 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
CH-457: AI for chemistryThe AI for Chemistry course will focus on teaching students how to use machine learning algorithms and techniques to analyze and make predictions about chemical data. The course will cover topics such
EE-311: Fundamentals of machine learningCe cours présente une vue générale des techniques d'apprentissage automatique, passant en revue les algorithmes, le formalisme théorique et les protocoles expérimentaux.
CS-421: Machine learning for behavioral dataComputer 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
ENG-466: Distributed intelligent systemsThe goal of this course is to provide methods and tools for modeling distributed intelligent systems as well as designing and optimizing coordination strategies. The course is a well-balanced mixture
MGT-424: Advanced data driven business analyticsThis course aims to provide graduate students a grounding in the methods, theory, mathematics and algorithms needed to apply machine learning techniques to in business analytics domain. The course cov
CS-526: Learning theoryMachine learning and data analysis are becoming increasingly central in many sciences and applications. This course concentrates on the theoretical underpinnings of machine learning.
