Course

MATH-231: Probability and statistics I

Related courses (241)
PHYS-758: Advanced Course on Quantum Communication
The aim of this doctoral course by Nicolas Sangouard is to lay the theoretical groundwork that is needed for students to understand how to take advantage of quantum effects for communication technolog
CS-233(a): Introduction to machine learning (BA3)
Machine 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
COM-622: Topics in information-theoretic cryptography
Information-theoretic methods and their application to secrecy & privacy. Perfect information-theoretic secrecy. Randomness extraction & privacy amplification. Secret key generation from common random
MATH-441: Robust and nonparametric statistics
In the decades from 1930 to 1950, many rank-based statistics were introduced. These methods were received with much interest, because they worked under weak conditions. Starting in the late 1950, a th
COM-712: Statistical Physics for Communication and Computer Science
The course introduces the student to notions of statistical physics which have found applications in communications and computer science. We focus on graphical models with the emergence of phase trans
ENV-508: Analysis and management of industrial risks
Présenter aux étudiants: 1 - les notions de base de l'accidentologie industrielle par le biais du traitement de cas concrets (processus chimiques, stockages pétroliers, gazoduc,...) 2 - la mise en oeu
ENV-400: Air pollution and climate change
A survey course describing the origins of air pollution and climate change
MGT-492: Data science and machine learning I
This class provides a hands-on introduction to data science and machine learning topics, exploring areas such as data acquisition and cleaning, regression, classification, clustering, neural networks,
MATH-425: Spatial statistics
In this course we will focus on stochastic approaches for modelling phenomena taking place in multivariate spaces. Our main focus will be on random field models and on statistical methods for model-ba
BIOENG-450: In silico neuroscience
"In silico Neuroscience" introduces students to a synthesis of modern neuroscience and state-of-the-art data management, modelling and computing technologies.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.