MICRO-401: Machine learning programmingThis is a practice-based course, where students program algorithms in machine learning and evaluate the performance of the algorithm thoroughly using real-world dataset. ...
MICRO-457: Materials processing with intelligent systemsRepeatability in laser material processing is challenging due to high-speed dynamics. To address this issue, the course provides an overview of laser theory, laser-material interaction, various types of sensors (acoustic & optic), data acquisition, online ...
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 ...
AR-302(ah): Théorie et critique du projet 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 ...
COM-490: Large-scale data science for real-world dataThis hands-on course covers tools and methods used by data scientists, from researching solutions to scaling prototypes on Spark clusters. Students engage with the full data engineering and data science pipeline, from data acquisition to extracting insight ...
CS-330: Intelligence artificielleIntroduction 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. ...
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-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. ...
MATH-474: Statistics for genomic data analysisAfter a short introduction to basic molecular biology and genomic technologies, this course covers the most useful statistical concepts and methods for the analysis of genomic data. ...
