## SME45 & M2S1: Statistical data analysis (TDS et Imagerie approfondissements)

The course is taught by AndrĂ© Ferrari.

### Desription

This course is taught to M2 imag2e & estel students.

This course will introduce the students to the fundamental concepts of detection, estimation
and inverse problems theory. It will particularly focus on the (asymptotic) performance analysis of the various algorithms. The course will also focus on convex optimization, as a central tool for
parameter estimation and inverse problems.

A large part of the course is devoted to practical projects, where the students will code various algorithms and compare theoretical results with simulation results.

### Piazza

All the annoucements will be posted on Piazza.
Students are also encouraged to post questions on Piazza.

### Homework

Students will have to complete three projects during the course.
You must submit for each project a pdf export of a jupyter notebook
including the code, plots and a detailed report.
Your are welcomed to work in pairs and to submit a single document.

Due dates will be posted on the website along with the assignments.
Late homework will not be accepted.

### Software

In this course we will be using Julia.
I encourage you to code (with zero setup time!) in the cloud using JuliaBox.

### Course textbooks

The course will cover parts of:

Fundamentals of Statistical Signal Processing, Volume 1: Estimation Theory, by Steven M. Kay, Prentice Hall, 1993. amazon.

Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory, by Steven M. Kay, Prentice Hall 1998. amazon.

Convex Optimization, by Stephen Boyd and Lieven Vandenberghe Cambridge University Press. book.