COM-406: Foundations of Data ScienceWe discuss a set of topics that are important for the understanding of modern data science but that are typically not taught in an introductory ML course. In particular we discuss fundamental ideas an
MATH-341: Linear modelsRegression modelling is a fundamental tool of statistics, because it describes how the law of a random variable of interest may depend on other variables. This course aims to familiarize students with
ENV-140: Fundamentals of geomaticsBases de la géomatique pour les ingénieur·e·s civil et en environnement. Présentation des méthodes d'acquisition, de gestion et de représentation des géodonnées. Apprentissage pratique avec des méthod
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 bio
MGT-418: Convex optimizationThis course introduces the theory and application of modern convex optimization from an engineering perspective.
MICRO-511: Image processing IIntroduction to the basic techniques of image processing. Introduction to the development of image-processing software and to prototyping using Jupyter notebooks. Application to real-world examples in
CS-233: Introduction to machine learningMachine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analy
CS-503: Visual intelligence : machines and mindsThe course will discuss classic material as well as recent advances in computer vision and machine learning relevant to processing visual data -- with a primary focus on embodied intelligence and visi
