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 in python using jupyter notebooks. ...
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. ...
EE-566: Adaptation and learningIn this course, students learn to design and master algorithms and core concepts related to inference and learning from data and the foundations of adaptation and learning theories with applications.
EE-559: Deep learningThis course explores how to design reliable discriminative and generative neural networks, the ethics of data acquisition and model deployment, as well as modern multi-modal models. ...
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. ...
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. ...
PHYS-642: Statistical physics for optimization & learningThis course covers the statistical physics approach to computer science problems, with an emphasis on heuristic & rigorous mathematical technics, ranging from graph theory and constraint satisfaction to inference to machine learning, neural networks and st ...
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 ...
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. ...
CIVIL-459: Deep learning for autonomous vehiclesDeep Learning (DL) is the subset of Machine learning reshaping the future of transportation and mobility. In this class, we will show how DL can be used to teach autonomous vehicles to detect objects, make predictions, and make decisions. (Fun fact: this s ...
BIOENG-456: Controlling behavior in animals and robotsStudents will acquire an integrative view on biological and artificial algorithms for controlling autonomous behaviors. Students will synthesize and apply this knowledge in oral presentations and computational exercises. ...
MICRO-512: Image processing IIStudy of advanced image processing; mathematical imaging. Development of image-processing software and prototyping in Jupyter Notebooks; application to real-world examples in industrial vision and biomedical imaging. ...
EE-612: Fundamentals in statistical pattern recognitionThis 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 source code) to PhD students for their work. ...
DH-406: Machine learning for DHThis course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and implement methods to analyze diverse data types, such as imag ...
CS-330: Artificial intelligenceIntroduction aux techniques de l'Intelligence Artificielle, complémentée par des exercices de programmation qui montrent les algorithmes et des exemples de leur application à des problèmes pratiques. ...
