This course is taught in English.
This course provides an introduction to techniques for constructing systems that exhibit human level intelligence. The first half of the class covers techniques for machine learning and pattern recognition, including Bayesian learning, clustering, support vector machines, artificial neural networks, and deep learning architectures. The second half of the class covers techniques for symbolic reasoning systems including planning and problem solving, diagnostic reasoning, rule based programming, reasoning with temporal and spatial relations, and causal reasoning with Bayesian networks.
The course includes a Neural Network programming exercise performed with Keras and PyTorch in Python.
1. Introduction to intelligent systems.
2. Performance Evaluations
3. Bayesian Machine learning
4. Non supervised learning and clustering with K-means and EM
5. Artificial Neural Networks and deep learning.
6. Decision Trees and random forests.
7. Planning and problem solving.
8. Rule based systems
9. Reasoning with spatial, temporal and other forms of relations.
10. Diagnostic and Causal reasoning with Bayesian networks and probabilistic graph models.
Basic Probability and Statistics
Experience with Python is a plus.
90% of the grade is a based on a 3 hour written exam, all notes and documents authorised.
The other 10% is based on the Neural Network programming exercise.
The second session exam is a 3 hour written exam, all notes and documents authorised.
N1=0.9 x E1 + 0.1 x TP
N2=0.9 x E2 + 0.1 x TP
L'examen existe uniquement en anglais
Le cours est programmé dans ces filières :
Code de l'enseignement : 4MMSIRR6
Langue(s) d'enseignement :
Le cours est rattaché aux structures d'enseignement suivantes :
Vous pouvez retrouver ce cours dans la liste de tous les cours.
mise à jour le 15 janvier 2017