Descriptive sheet of the TU INF-GRAM

TU INF-GRAM - Artificial intelligence programming

Objective

To show the student how to use in practice the main artificial intelligence based methodologies to analyse health data.

Knowledge to be acquired

Main machine learning algorithms and methodologies to deal with health data and how to implement them in practice using Python.

Skills to be acquired

Being able to develop an artificial intelligence based solution dealing with a health dataset using python.

Description
  • Introduction to Machine learning applied to health data.
  • Supervised learning: Main Classification approaches.
  • Model Evaluation.
  • Non supervised learning: Main Clustering methods.
  • Regularization.

Pedagogical method

Courses on-line by NetConference, distance mentoring over the Internet.

Pre-requisite

Basic knowledge of programming, having done the module INF-PROF or equivalent.

Total hours

Around 18 hours per NetConference + personal work.

ECTS

4

Place

Faculté des sciences médicales et paramédicale de Marseille

Dates

See the schedule for this TU 

Head

R. Ureña

Teachers

R. Ureña

TU – mandatory

This TU is mandatory for the Master 2 of Public Health:

  • AI4PH: "Artificial Intelligence for Public Health"

This TU is mandatory for the postgraduate diploma DESU:

  • AI4PH: "Artificial Intelligence for Public Health"

This TU is mandatory for the postgraduate diploma CESU:

  • AIPro: "Artificial Intelligence Programming in Python"

TU – optional

  

Assessment of knowledge

A single examination is organised, in accordance with the Aix Marseille University rule.

For this single session, the assessment of knowledge of this teaching unit is based on:

  • Continuous assessment of knowledge with various homework assignments, tests and exercises. This continuous assessment will have a coefficient of 2 in the final mark.
  • An oral examination. This exam will have a coefficient of 3 in the final grade. A personal convocation will be sent by e-mail to each student.

Within the framework of the Master's degree, the threshold mark for the UE is set at 8. If the overall mark obtained is ≥ 8 and < 10, the UE can be compensated if the overall average of the semester is ≥ 10.

In the framework of the DESU and of the CESU, there is no compensation between UE.

Educational resources

Course materials on the Internet.