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-608: Deep Learning For Natural Language ProcessingThis course covers advanced topics in deep learning architectures for natural language processing. The focus is on attention-based architectures, structure processing and variational-Bayesian approaches, and why these models are particularly suited to the ...
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-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-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. ...
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 ...
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. ...
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 ...
MSE-305: Introduction to atomic-scale modelingThis course provides an introduction to the modeling of matter at the atomic scale, using interactive Jupyter notebooks to see several of the core concepts of materials science in action. ...
EE-735: Online learning in gamesThis course provides an overview of recent developments in online learning, game theory, and variational inequalities and their point of intersection with a focus on algorithmic development. The primary approach is to lay out the different problem classes ...
AR-201(bc): Studio BA3 (Peris et Toral)In this studio, students will establish the foundational comprehending needed to design collective housing typologies by understanding and applying Louis Kahn's principles of "served and servant spaces. ...
AR-202(bc): Studio BA3 (Peris et Toral)In this studio, students will establish the foundational comprehending needed to design collective housing typologies by understanding and applying Louis Kahn's principles of "served and servant spaces. ...
CS-442: Computer visionComputer Vision aims at modeling the world from digital images acquired using video or infrared cameras, and other imaging sensors.
We will focus on images acquired using digital cameras. We will introduce basic processing techniques and discuss their fie ...
AR-302(ah): Studio BA6 (Malterre-Barthes)How is architecture made? 'Revolutionize Construction' unpacks the political economy of construction, examining who commissions, finances, and builds, and interrogating architects' roles. It explores how these systems might be revolutionized to enable the ...
CS-423: Distributed information systemsThis course introduces the foundations of information retrieval, data mining and knowledge bases, which constitute the foundations of today's Web-based distributed information systems. ...
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 ...
