Course Overview

This course completes the basis of computer programming applied to the
simulation of stochastic processes.

Learning Achievement

Competence

Course prerequisites

Grading Philosophy

> Intermediate assessment (mark I out of 20, duration 1h30 to 2h40
during the semester) and final exam (mark E out of 20, duration 3h in
December)

- final mark I/3+2E/3

> Rules and protocol for failures/re-sits is clearly described: in
case of failure, student have access to a second exam(mark S)

- the final mark is then max(I/3+2S/3,S)

Course schedule

Course content includes: 
- Monte Carlo methods and stochastic optimization algorithms -
bayesian statistics

The course will apply problems in data analysis and signal and image
processing (denoising, deconvolution, classification, segmentation,
clustering). 

Course type

Lectures

Online Course Requirement

Instructor

Other information

Mode of delivery: Face-to-face teaching

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