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. ...
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 machine learning. ...
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-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. ...
CH-242(b): Statistical mechanics for chemistryCe cours construit la base théorique universelle permettant de comprendre les systèmes à grand nombre de particules. Les méthodes introduites sont utilisées pour éclairer de nombreux phénomènes à travers la chimie. ...
PHYS-424: Plasma IIThis course completes the knowledge in plasma physics that students have acquired in the previous two courses, with a discussion of different applications, in the fields of magnetic confinement and controlled fusion, astrophysical and space plasmas, and so ...
EE-512: Applied biomedical signal processingThe goal of this course is twofold: (1) to introduce physiological basis, signal acquisition solutions (sensors) and state-of-the-art signal processing techniques, and (2) to propose concrete examples of applications for vital sign monitoring and diagnosis ...
MGT-418: Convex optimizationThis course introduces the theory and application of modern convex optimization from an engineering perspective. ...
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. ...
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 ...
FIN-616: Financial Econometrics II (2020 -2024)This course has 3 parts
- We understand how to use moment based estimations to obtain the parameters for explicit or implicit models.
- We learn how to estimate latent parameters in a time series context with the Kalman filter.
- Machine learning tools bel ...
FIN-423: Financial machine learning projectsThe objective of this course is to acquire experience in financial machine learning by solving real-world problems. Different groups of students will work on different industry projects during the semester. Lectures will discuss best practices and tools. ...
FIN-406: MacrofinanceThis course provides students with a working knowledge of macroeconomic models that explicitly incorporate financial markets. The goal is to develop a broad and analytical framework for analyzing the interaction of financial decisions, macroeconomic events ...
MGT-448: Statistical inference and machine learningThis 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 topics from machine learning, classical statistics, and data ...
