Catégorie

Apprentissage automatique

Cours associés (163)

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MATH-524: Nonparametric estimation and inference

Nonparametric models are used to identify nonlinear relationships within data. This course gives a graduate-level overview of nonparametric statistical estimation and inference theory.

MATH-250: Advanced numerical analysis I

Construction and analysis of numerical methods for the solution of problems from linear algebra, integration, approximation, and differentiation.

CS-451: Distributed algorithms

Computing is nowadays distributed over several machines, in a local IP-like network, a cloud or a P2P network. Failures are common and computations need to proceed despite partial failures of machin

MATH-232: Probability and statistics (for IC)

A basic course in probability and statistics

BIOENG-519: Methods: omics in biomedical research

High-throughput methodologies broadly called Omics allow to characterize the complexity and dynamics of any biological system. This course will provide a general description of different methods relat

BIO-482: Neuroscience: cellular and circuit mechanisms

This course focuses on the biophysical mechanisms of mammalian brain function. We will describe how neurons communicate through synaptic transmission in order to process sensory information ultimately

NX-435: Systems neuroscience

The course "Systems Neuroscience" explores neural circuits and networks to understand how groups of neurons process information and generate behavior. It integrates techniques from neurophysiology, an

CS-431: Introduction to natural language processing

The objective of this course is to present the main models, formalisms and algorithms necessary for the development of applications in the field of natural language information processing. The concept

MATH-383: Risk and environmental sustainability

This course gives an introduction to the assessment of risk with a particular focus on modelling of rare events, which can have huge environmental impacts.

MGT-448: Statistical inference and machine learning

This 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 topi