Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Graph
Search

Course

MATH-600: Optimization and simulation

Summary

Master state-of-the art methods in optimization with heuristics and simulation.

Work involves:

reading the material beforehand
class hours to discuss the material and solve problems
homework

Lectures

Instructors

Official source

https://edu.epfl.ch/coursebook/en/optimization-and-simulation-MATH-600

Moodle Page

http://go.epfl.ch/MATH-600

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Summary

Master state-of-the art methods in optimization with heuristics and simulation.

Work involves:

reading the material beforehand
class hours to discuss the material and solve problems
homework

Lectures

Instructors

Official source

https://edu.epfl.ch/coursebook/en/optimization-and-simulation-MATH-600

Moodle Page

http://go.epfl.ch/MATH-600

About this result

Lectures in this course (2)

Optimization and Simulation: Introduction to Simulation

Introduces state-of-the-art methods in optimization and simulation, covering topics like statistical analysis, variance reduction, and simulation projects.

Simulation & Optimization: Poisson Process & Random Numbers

Explores simulation pitfalls, random numbers, discrete & continuous distributions, and Monte-Carlo integration.

Related courses (30)

EE-556: Mathematics of data: from theory to computation

This course provides an overview of key advances in continuous optimization and statistical analysis for machine learning. We review recent learning formulations and models as well as their guarantees

COM-500: Statistical signal and data processing through applications

Building up on the basic concepts of sampling, filtering and Fourier transforms, we address stochastic modeling, spectral analysis, estimation and prediction, classification, and adaptive filtering, with an application oriented approach and hands-on numeri ...

PHYS-467: Machine learning for physicists

Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practised. ...

CS-401: Applied data analysis

This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the data science world (pandas, scikit-learn, Spark, etc.) ...

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 practically implemented. ...