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, return prediction, and textual analysis. A particular focus will be on deep learning and the practical details o ...
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 practised. ...
DH-405: Foundations of digital humanitiesThis course introduces the core concepts and methodologies of Digital Humanities, integrating both theoretical and practical perspectives. Students learn to work with large-scale cultural datasets, acquiring the tools and techniques required for their proc ...
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 practically implemented. ...
EE-411: Fundamentals of inference and learningThis is an introductory course in the theory of statistics, inference, and machine learning, with an emphasis on theoretical understanding & practical exercises. The course will combine, and alternate, between mathematical theoretical foundations and prac ...
CS-401: Applied data analysisThis course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the data science world (pandas, scikit-learn, Spark, etc.) ...
ME-390: Foundations of artificial intelligenceThis 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. ...
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. ...
MGT-424: Machine learning methods in econometricsThis 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 covers topics from machine learning, classical statistics, ...
PHYS-754: Lecture series on scientific machine learningThis lecture presents ongoing work on how scientific questions can be tackled using machine learning. Machine learning enables extracting knowledge from data computationally and in an automatized way. We will learn on examples how this is influencing the v ...
CS-503: Visual intelligenceThis course covers both classic concepts and recent advances in computer vision and machine learning for processing visual data -- with a primary focus on embodied intelligence and multimodal learning ...
CS-625: Transfer learning and meta-learningThis seminar course covers principles and recent advancements in machine learning methods that have the ability to solve multiple tasks and generalize to new domains in which training and test distributions are different. ...
ENG-704: EECS Seminar: Advanced Topics in Machine LearningStudents learn about advanced topics in machine learning, artificial intelligence, optimization, and data science. Students also learn to interact with scientific work, analyze and understand strengths and weaknesses of scientific arguments of both theoret ...
ENV-513: Multivariate statistics in REnvironmental datasets often contain numerous parameters. Multivariate statistics allow us to simultaneously explore, understand and model such datasets. This course provides conceptual introduction and guidelines for applying multivariate statistical tool ...
EE-626: Graph representations for biology and medicineSystems of interacting entities, modeled as graphs, are pervasive in biology and medicine. The class will cover advanced topics in signal processing and machine learning on graphs and networks, and will showcase applications of the tools in biomedicine. ...
