Systèmes intelligents : reconnaissance et raisonnement (en anglais) - 4MMSIRR6
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CM : 15.75
TD : 15.75
TP : -
Projet : -
Stage : -
DS : -
Crédits ECTS : 3.0
Responsables :James CROWLEY
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 Linear Algebra Experience with Python is a plus.
Contrôles des connaissances
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 :
Cursus ingénieur - Filière ISI - Semestre 8 (ce cours est donné uniquement en anglais )