Number of hours
- Lectures 10.0
- Projects -
- Tutorials -
- Internship -
- Laboratory works 32.0
- Written tests -
ECTS
ECTS 0.6
Goal(s)
Know the different principles of machine learning and the associated algorithms.
Know how to apply these algorithms to a concrete problem.
Be able to choose the type of algorithm best suited to the problem at hand.
Take a critical look at available data
be able to analyze the results obtained
Content(s)
Chapter 1: Bayesian learning (learning by estimating probability densities)
Chapter 1: Evaluation of a decision system, Performance comparison
Chapter 1: Choosing the representation space
Chapter 2: Decision trees (learning by combining decisions)
Chapter 3: Learning by direct computation of boundaries (learning by optimization)
Signal processing, data processing, statistics
Test
RENDU, EXAM
Calendar
The course exists in the following branches:
- Curriculum - TIS - Semester 9
Additional Information
Course ID : KATP9M20
Course language(s): 
You can find this course among all other courses.
Bibliography
Statistical pattern recognition K. Fukunaga, Academic Press
Decision, estimation and classification ? An introduction to pattern recognition and related topics, C. Therrien, Wiley
Diagnostic et reconnaissance de formes, B. Dubuisson, Hermes
Kernel methods for pattern analysis, J. Shawe-Taylor, N. Christianini, Cambridge university press
An introduction to support vector machines and other kernel-based learning methods, N. Christianini, J. Shawe-Taylor, Cambridge university press
Réseaux neuronaux, JP; Bernard, Vuibert
Graphes d?induction, Apprentissage et data-mining, D. Zighed et R. Rakotomalala, Hermes
Learning and soft computing, V. Kecman, MIT Press
Apprentissage artificiel, concepts et algorithmes, A. Cornuejols, L. Miclet, Eyrolles
Apprentissage artificiel: Deep learning, concepts et algorithmes Vincent Barra , Laurent Miclet; A. Cornuejols, L. Miclet, Eyrolles
Bases théoriques pour l?apprentissage et la reconnaissances des formes, A. de Beauville, F.Z. Kettaf, Cépadues