Catégorie

Traitement automatique du langage naturel

Cours associés (140)

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MATH-205: Analysis IV - Lebesgue measure, Fourier analysis

Learn the basis of Lebesgue integration and Fourier analysis

CS-433: Machine learning

Machine 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 pr

HUM-432: How people learn: Designing Learning Tools I

The students will understand the cognitive and social factors which affect learning - particularly in science and engineering. They will be able to use social research techniques as part of the design

CS-320: Computer language processing

We teach the fundamental aspects of analyzing and interpreting computer languages, including the techniques to build compilers. You will build a working compiler from an elegant functional language in

BIO-205: Cellular and molecular biology I

The course covers the regulation of gene expression, which translates the information contained in the genome into function, by adjusting the levels and activities of mRNAs and proteins to the needs o

PHYS-101(en): General physics : mechanics (English)

Students will learn the principles of mechanics to enable a better understanding of physical phenomena, such as the kinematics and dyamics of point masses and solid bodies. Students will acquire the c

COM-308: Internet analytics

Internet analytics is the collection, modeling, and analysis of user data in large-scale online services, such as social networking, e-commerce, search, and advertisement. This class explores a number

MATH-111(e): Linear Algebra

L'objectif du cours est d'introduire les notions de base de l'algèbre linéaire et ses applications.

PHYS-441: Statistical physics of biomacromolecules

Introduction to the application of the notions and methods of theoretical physics to problems in biology.

COM-406: Foundations of Data Science

We 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