Artificial Intelligence - 5MMIA

Number of hours
- Lectures 18.0
- Projects -
- Tutorials -
- Internship -
- Laboratory works -
- Written tests -
ECTS
ECTS 2.0

Goal(s)

Acquiring the main competences in machine learning and artificial intelligence
Acquiring a general background in data science so as to be able to interact with experts in statistical learning theory and artificial intelligence
Knowing one or two subfields of Artificial Intelligence

Responsible(s)

Alhame DOUZAL

Content(s)

General Introduction to Artificial Intelligence (1h30 CM)
Symbolic AI (3h CM + 1h30TP)
1. Constraint Satisfaction Problems, Search
2. Multiagent Systems
Machine Learning (9h CM + 3h TP)
1. Metrics (3h CM) :
  - Metrics for unstructured data
2. Non supervised Learning (3h CM) :
  - Partitioning methods (k-means, PAM),
  - Hierarchical methods (CAH, Divisive),
  - Under constraints (SOM)
3. Supervised Learning (3h CM) :
  - Regression/Classification tree (CART),
  - Neareste Neighbours (kNN),
  - Validation Theory in Classification

Prerequisites

General Mathematical and Computational background

Test

N1 = E1
N2 = E2

N1=1st session grade
N2=2nd session grade
E1=1st session exam (2h written exam)
E2=2nd session exam (written or oral, depending on the number of students

N1 = E1
N2 = E2

Codification pour la formule de calcul de la note :
N1=note finale de 1ère session
N2=note finale de 2ème session
E1=examen de 1ère session (2h d'écrit)
E2=examen de 2ème session (2h écrit ou oral)

Calendar

The course exists in the following branches: