Number of hours
- Lectures 12.0
- Projects -
- Tutorials -
- Internship -
- Laboratory works 36.0
- Written tests -
ECTS
ECTS 0.4
Goal(s)
Discover automatic learning algorithms and be able to implement them effectively on concrete problems.
Content(s)
Chapter 1 :
- Bayesian learning (learning by estimating probability densities): linear or quadratic classifiers, kppV,...
- Evaluation of a decision system, Comparison of algorithm performance
- The choice of the representation space
Chapter 2: Learning by direct border calculation (learning by optimization): neural networks, SVM, introduction to deep learning (CNN)
Chapter 3: Decision trees (learning by combining decisions): induction of decision trees (C4.5, CART...), random forest
Chapter 4: Unsupervised classification (learning by similarity): CAH, k-means, GMM
Test
EXAM, SOUT
Calendar
The course exists in the following branches:
- Curriculum - IESE - Semester 9
Additional Information
Course ID : KAIE9M15
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